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st: Reading the output of xtmelogit


From   "Jackson, Theron Keith (UMSL-Student)" <tkjgx2@umsl.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Reading the output of xtmelogit
Date   Tue, 20 Oct 2009 17:14:12 -0500

I ran the following model and I am having trouble reading the hierarchial part of the data.  I understand the individual level characteristics but do not understand if nghd disadvantage is significant for the county or for the zip code. I own the book multilevel and longitudnal modeling using stata, but they do not go into detail on the output of the xtmelogit model.  Any help would be much appreciated.  
 
 
Thanks,
 
 
 
Theron

. xtmelogit Cong_Crack_Cocaine Age Black Hispanic Indian Other_Race Severe1 School2 Unemployment Other_Work
>  Married Separated Homless Other_Residence Inpatient_Tx Outpatient_Tx Arrest Jail ///
>         Dependency Int_Resp_Age Int_Resp_Gender Charge_Violent Charge_Property Charge_Other || county: Co
> _Nghd_Dis || RESIDZIP: Nghd_Dis, variance 
Refining starting values: 
Iteration 0:   log likelihood = -2376.3809  (not concave)
Iteration 1:   log likelihood = -2351.6708  
Iteration 2:   log likelihood = -2304.5625  
Performing gradient-based optimization: 
Iteration 0:   log likelihood = -2304.5625  
Iteration 1:   log likelihood = -2292.9815  (not concave)
Iteration 2:   log likelihood = -2290.5132  
Iteration 3:   log likelihood = -2289.2897  
Iteration 4:   log likelihood = -2289.0232  
Iteration 5:   log likelihood = -2289.0208  
Iteration 6:   log likelihood = -2289.0208  
Mixed-effects logistic regression               Number of obs      =      3861
--------------------------------------------------------------------------
                |   No. of       Observations per Group       Integration
 Group Variable |   Groups    Minimum    Average    Maximum      Points
----------------+---------------------------------------------------------
         county |      196          1       19.7        281           7
       RESIDZIP |     1270          1        3.0         27           7
--------------------------------------------------------------------------
                                                Wald chi2(23)      =    554.42
Log likelihood = -2289.0208                     Prob > chi2        =    0.0000
------------------------------------------------------------------------------
Cong_Crack~e |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         Age |   .5085038    .042903    11.85   0.000     .4244154    .5925922
       Black |   .2516724   .0886922     2.84   0.005     .0778389    .4255059
    Hispanic |  -.4572121   .1197924    -3.82   0.000    -.6920008   -.2224234
      Indian |    .017753   .3164907     0.06   0.955    -.6025574    .6380635
  Other_Race |  -.2039618   .2765931    -0.74   0.461    -.7460743    .3381507
     Severe1 |   .0010094   .0635329     0.02   0.987    -.1235128    .1255315
     School2 |   .0528132   .0488388     1.08   0.280    -.0429092    .1485355
Unemployment |    -.31175    .078181    -3.99   0.000     -.464982    -.158518
  Other_Work |   .2927659   .1336319     2.19   0.028     .0308523    .5546796
     Married |  -.1355542   .0964009    -1.41   0.160    -.3244965    .0533881
   Separated |  -.1180146   .1027922    -1.15   0.251    -.3194835    .0834543
     Homless |   -.692701   .1447875    -4.78   0.000    -.9764793   -.4089228
Other_Resi~e |  -.0991939   .1515966    -0.65   0.513    -.3963178      .19793
Inpatient_Tx |   .6023429   .0811455     7.42   0.000     .4433007    .7613851
Outpatient~x |   .1074848    .088585     1.21   0.225    -.0661387    .2811083
      Arrest |  -.1030657   .1621974    -0.64   0.525    -.4209667    .2148353
        Jail |  -.2018198   .1411266    -1.43   0.153    -.4784229    .0747832
  Dependency |   1.312378    .096314    13.63   0.000     1.123606     1.50115
Int_Resp_Age |  -.1239394    .051547    -2.40   0.016    -.2249698   -.0229091
Int_Resp_G~r |  -.1627547   .0893628    -1.82   0.069    -.3379026    .0123933
Charge_Vio~t |   .0829887   .1133725     0.73   0.464    -.1392172    .3051947
Charge_Pro~y |  -.0511462   .1037836    -0.49   0.622    -.2545583    .1522658
Charge_Other |  -.0185515   .3706106    -0.05   0.960    -.7449349    .7078319
       _cons |  -1.547129     .76264    -2.03   0.042    -3.041876   -.0523824
------------------------------------------------------------------------------
------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
county: Independent          |
               var(Co_Ngh~s) |   3.468035   2.131447      1.039768    11.56725
                  var(_cons) |   7.75e-17   1.75e-09             0           .
-----------------------------+------------------------------------------------
RESIDZIP: Independent        |
               var(Nghd_Dis) |   3.65e-13   1.93e-06             0           .
                  var(_cons) |   8.22e-16   1.21e-08             0           .
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(4) =     8.27   Prob > chi2 = 0.0821
Note: LR test is conservative and provided only for reference.
 

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