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Re: st: xtlogit vs gllamm


From   "Bryan W. Griffin" <bwgriffin@GeorgiaSouthern.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: xtlogit vs gllamm
Date   Tue, 16 May 2006 10:07:06 -0400

There are three reasons results from xtlogit and gllamm do not agree here for you.

First, your data apparently have a large random effect variance which creates problems, often, with random effect estimation for logistic regression. Or it may be simply that you have spare data (e.g., little information, small sample size, or few 0 or 1's for the outcome). The condition code from gllamm you include suggest and the large differences in parameter estimates suggest something odd with your data. Most likey the issue is the large random effect -- note rho is about .78 (that is very large for logistic regression).

Second, xtlogit and gllamm use different implementations of adaptive quadrature. I have run simulations on this and reported to Stata that xtlogit's adaptive quadrature provides large biased estimates when large random effects are present and that gllamm estimates and estimates using adaptive quadrature from SAS are more accurate (and agree with each other). I hope that with a future update the implementation in xtlogit changes to agree more with gllamm and SAS. Note, however, that xtlogit with non-adaptive quadrature works very well, so try estimating your model with that approach.

Third, despite belief among some that only a few integration points are needed, when large variances are present in random effects, many integration points are required. You used only 12; up the number to 150 for gllamm and xtlogit then check estimates. Rerun both with number of points at 165 or 170 for comparisons to see if number of points still produces differing results. Run xtlogit with both adaptive and nonadaptive, and do the same with gllamm and compare results.




Date: Mon, 15 May 2006 14:49:33 -0400
From: =?iso-8859-1?Q?B=E9gin_Karine?= <k.begin@umontreal.ca>
Subject: st: xtlogit vs gllamm

Hi,

I have a dichotomous outcome variable taken at various time points. I performed a regression on this variable using both the "gllamm" command with a logit link, a binomial distribution and the adaptative quadrature (ado file) and the "xtlogit" command (running with the same number of adaptative quadrature points as with gllamm). Both commands produce quiet different results and I am wondering why since both gllamm and xtlogit seem to be conceived for similar purpose. An example of the results is listed below.

Thanks,
Karine


. gllamm emploi sexe FAC3_, i(no_seque) link(logit) family(binom) nip(12) adapt
Condition Number = 8.8565296
- ------------------------------------------------------------------------------
emploi | Coef. Std. Err. z P>|z| [95% Conf. Interval]
- -------------+----------------------------------------------------------------
sexe | -2.772054 .3028192 -9.15 0.000 -3.365569 -2.17854
FAC3_ | 1.382007 .1186971 11.64 0.000 1.149365 1.614649
_cons | -9.172404 .4245712 -21.60 0.000 -10.00455 -8.34026
- ------------------------------------------------------------------------------

Variances and covariances of random effects
- ------------------------------------------------------------------------------
***level 2 (no_seque)
var(1): 227.29026 (21.232383)
- ------------------------------------------------------------------------------


xtlogit
. xtlogit emploi sexe FAC3_, i(no_seque)
- ------------------------------------------------------------------------------
emploi | Coef. Std. Err. z P>|z| [95% Conf. Interval]
- -------------+----------------------------------------------------------------
sexe | -.1323433 .1503605 -0.88 0.379 -.4270445 .1623579
FAC3_ | .2813436 .065331 4.31 0.000 .1532971 .4093901
_cons | -2.290372 .085298 -26.85 0.000 -2.457553 -2.123191
- -------------+----------------------------------------------------------------
/lnsig2u | 2.481279 .0429478 2.397103 2.565455
- -------------+----------------------------------------------------------------
sigma_u | 3.457824 .074253 3.31531 3.606463
rho | .7842202 .0072676 .7696356 .7981235
- ------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) = 7631.32 Prob >= chibar2 = 0.000
___________________________________________________________________
Bryan W. Griffin
Curriculum, Foundations, & Reading
P.O. Box 8144
Georgia Southern University
Statesboro, GA 30460-8144

Phone: 912-681-0488
E-Mail: bwgriffin@GeorgiaSouthern.edu
WWW: http://coe.georgiasouthern.edu/foundations/bwgriffin/

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