st: -xtmelogit- question

["Garrard, Wendy M." <wendy.garrard@Vanderbilt.Edu>]

I am using -xtmelogit- to estimate a crossed-effects random intercept
model.  The data represents responses from individuals who use services
at different agencies; those agencies operate in multiple counties.  I
want to identify the county-specific effects and the agency-specific
effects.   There are 95 total counties in TN, but only 70 have
respondents; There are 20 agencies in the data. (-tab- confirms this.)
	
	PROBLEM -- when I run the first command below , the groupings
section of the output show 70 counties but 93 agencies and the RE
estimates for the var output are puzzling (see the example output below
my signature):
	
	        xtmelogit outcome varlist || tncounty: || agencynum: ,
options
	
	
	Yet, when I run this second version of the command, mimicking
the new Stata10 manual for treating the agencies as if nested in
counties, it gives me the correct number of agencies in the groupings
section of the output and the RE estimates for the var/std are sensible.
	
	        xtmelogit outcome varlist || _all:R.tncounty ||
agencynum: , options
	
		
	MY THOUGHTS -- judging from the missing value in the RE table, I
wonder if there is some sort of problem maximizing/estimating, but I'm
not sure how or why.   What exactly does the  -_all:R.-  trick actually
do?   I see from the documentation that it's reducing the size of the
matrix being manipulating, but is this simply a way to tell Stata not to
waste effort estimating the 70 different county effects?  It's not clear
to me how this should affect its counting of agencies, unless there is
something extremely complicated about the nesting structure of the data.
For example,  every respondent has a county and an agency, but there are
a few "one-agency counties" that are anomalous;  so maybe the _all trick
fixes things reducing the complexity of having the individual counties,
freeing it act properly with the agencies. 

Thanks in advance for your suggestions.

See example output below.

Regards,
Wendy
********************
	
	. xtmelogit vsat black || tncounty: || agencynum: , or variance
cov(un)
	Note: single-variable random-effects specification; covariance
structure set to
	      identity
	
	Refining starting values: 
	
	Iteration 0:   log likelihood = -1721.4209  (not concave)
	Iteration 1:   log likelihood = -1702.8252  
	Iteration 2:   log likelihood = -1697.1018  
	
	Performing gradient-based optimization: 
	
	Iteration 0:   log likelihood = -1697.1018  
	Iteration 1:   log likelihood =  -1696.955  
	Iteration 2:   log likelihood = -1696.9098  
	Iteration 3:   log likelihood = -1696.8852  
	Iteration 4:   log likelihood = -1696.8833  
	Iteration 5:   log likelihood = -1696.8832  
	
	Mixed-effects logistic regression               Number of obs
=      2467
	
	
------------------------------------------------------------------------
--
	                |   No. of       Observations per Group
Integration
	 Group Variable |   Groups    Minimum    Average    Maximum
Points
	
----------------+-------------------------------------------------------
--
	       tncounty |       70          1       35.2        411
7
	      agencynum |       93          1       26.5        245
7
	
------------------------------------------------------------------------
--
	
	                                                Wald chi2(1)
=      1.84
	Log likelihood = -1696.8832                     Prob > chi2
=    0.1751
	
	
------------------------------------------------------------------------
------
	        vsat | Odds Ratio   Std. Err.      z    P>|z|     [95%
Conf. Interval]
	
-------------+----------------------------------------------------------
------
	       black |   .8621235   .0943249    -1.36   0.175
.6957282    1.068315
	
------------------------------------------------------------------------
------
	
	
------------------------------------------------------------------------
------
	  Random-effects Parameters  |   Estimate   Std. Err.     [95%
Conf. Interval]
	
-----------------------------+------------------------------------------
------
	tncounty: Identity           |
	                  var(_cons) |   2.30e-12   1.34e-06
0           .
	
-----------------------------+------------------------------------------
------
	agencynum: Identity          |
	                  var(_cons) |   .1637944   .0756035
.0662835    .4047555
	
------------------------------------------------------------------------
------
	LR test vs. logistic regression:     chi2(2) =    16.75   Prob >
chi2 = 0.0002
	
	Note: LR test is conservative and provided only for reference.
	


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