When using the following code
Code:
melogit goodprog i.screen i.dxagecat  i.remoteness || sa3: , or
I obtain the following output. What I don't understand is why the LR test statistic is 0 comparing with the logistic model.

The variable goodprog is 0/1, with 34% of records=1. the general data characteristics are consistent with the first example in the stata melogit documentation.

Any suggestions would be very appreciated.

Thanks.

Mixed-effects logistic regression Number of obs = 3,074
Group variable: sa3 Number of groups = 80

Obs per group:
min = 8
avg = 38.4
max = 124

Integration method: mvaghermite Integration pts. = 7

Wald chi2(7) = 270.05
Log likelihood = -1826.6925 Prob > chi2 = 0.0000
------------------------------------------------------------------------------------
goodprog | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
1.screen | 3.380587 .288287 14.28 0.000 2.86025 3.995583
|
dxagecat |
2. 40-49 | 2.532121 .6644552 3.54 0.000 1.51398 4.234954
3. 50-59 | 2.312459 .6022525 3.22 0.001 1.387998 3.852649
4. 60-69 | 2.757566 .7171189 3.90 0.000 1.656405 4.590765
5. 70-79 | 3.076532 .8337443 4.15 0.000 1.808775 5.232849
|
remoteness |
2. Inner regional | .9092641 .1170525 -0.74 0.460 .7064999 1.170221
3. Major city | 1.221622 .1362389 1.79 0.073 .9817663 1.520076
|
_cons | .0964161 .025415 -8.87 0.000 .0575142 .1616308
-------------------+----------------------------------------------------------------
sa3 |
var(_cons)| 1.14e-33 3.75e-18 . .
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline odds (conditional on zero random effects).
LR test vs. logistic model: chi2(0) = 0.00 Prob > chi2 = .

Note: LR test is conservative and provided only for reference.