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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
. gllamm emploi sexe FAC3_, i(no_seque) link(logit) family(binom) nip(12) adapt
Running adaptive quadrature
Iteration 0: log likelihood = -5998.8654
Iteration 1: log likelihood = -3067.6979
Iteration 2: log likelihood = -2941.096
Iteration 3: log likelihood = -3064.9737
Iteration 4: log likelihood = -2937.5981
Iteration 5: log likelihood = -2957.6985
Iteration 6: log likelihood = -2969.5623
Iteration 7: log likelihood = -3040.5152
Iteration 8: log likelihood = -3040.4834
Iteration 9: log likelihood = -3039.0249
Iteration 10: log likelihood = -3037.5997
Iteration 11: log likelihood = -3036.2361
Iteration 12: log likelihood = -3035.5158
Iteration 13: log likelihood = -3034.6755
Iteration 14: log likelihood = -3034.3058
Iteration 15: log likelihood = -3034.0816
Iteration 16: log likelihood = -3033.572
Iteration 17: log likelihood = -3033.3959
Iteration 18: log likelihood = -3032.6627
Iteration 19: log likelihood = -3032.346
Iteration 20: log likelihood = -3032.165
Iteration 21: log likelihood = -3032.041
Iteration 22: log likelihood = -3031.9455
Iteration 23: log likelihood = -3031.8731
Iteration 24: log likelihood = -3031.8181
Iteration 25: log likelihood = -3031.7767
Iteration 26: log likelihood = -3031.7515
Iteration 27: log likelihood = -3031.7283
Iteration 28: log likelihood = -3031.7445
Iteration 29: log likelihood = -3031.7501
Iteration 30: log likelihood = -3031.7754
Iteration 31: log likelihood = -3031.7591
Iteration 32: log likelihood = -3031.966
Iteration 33: log likelihood = -3031.9902
Iteration 34: log likelihood = -3031.9543
Iteration 35: log likelihood = -3031.9529
Adaptive quadrature has converged, running Newton-Raphson
Iteration 0: log likelihood = -2901.8159
Iteration 1: log likelihood = -2901.7724
Iteration 2: log likelihood = -2901.772
Iteration 3: log likelihood = -2901.772
number of level 1 units = 12328
number of level 2 units = 1541
Condition Number = 8.8565296
gllamm model
log likelihood = -2901.772
------------------------------------------------------------------------------
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)
Fitting comparison model:
Iteration 0: log likelihood = -7147.7101
Iteration 1: log likelihood = -7143.665
Iteration 2: log likelihood = -7143.664
Fitting full model:
tau = 0.0 log likelihood = -7143.664
tau = 0.1 log likelihood = -6526.5334
tau = 0.2 log likelihood = -5998.8654
tau = 0.3 log likelihood = -5542.1559
tau = 0.4 log likelihood = -5136.8485
tau = 0.5 log likelihood = -4767.0482
tau = 0.6 log likelihood = -4419.9345
tau = 0.7 log likelihood = -4082.7112
tau = 0.8 log likelihood = -3736.9038
Iteration 0: log likelihood = -4084.348
Iteration 1: log likelihood = -3578.8779
Iteration 2: log likelihood = -3399.3091
Iteration 3: log likelihood = -3330.9429
Iteration 4: log likelihood = -3328.0047
Iteration 5: log likelihood = -3328.0046
Random-effects logistic regression Number of obs = 12328
Group variable (i): no_seque Number of groups = 1541
Random effects u_i ~ Gaussian Obs per group: min = 8
avg = 8.0
max = 8
Wald chi2(2) = 19.27
Log likelihood = -3328.0046 Prob > chi2 = 0.0001
------------------------------------------------------------------------------
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
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