vocab results

. binreg compnouc c.wingage , or n(20)

Iteration 1:   deviance =  1118.075
Iteration 2:   deviance =  1079.917
Iteration 3:   deviance =  1079.575
Iteration 4:   deviance =  1079.575
Iteration 5:   deviance =  1079.575

Generalized linear models                          No. of obs      =       376
Optimization     : MQL Fisher scoring              Residual df     =       374
                   (IRLS EIM)                      Scale parameter =         1
Deviance         =  1079.574935                    (1/df) Deviance =  2.886564
Pearson          =  1080.320194                    (1/df) Pearson  =  2.888557

Variance function: V(u) = u*(1-u/20)               [Binomial]
Link function    : g(u) = ln(u/(20-u))             [Logit]

                                                   BIC             = -1138.091

------------------------------------------------------------------------------
             |                 EIM
    compnouc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     wingage |      1.145      0.008    20.45   0.000        1.130       1.160
       _cons |      0.110      0.020   -12.33   0.000        0.077       0.156
------------------------------------------------------------------------------

. margins, at((p25) wingage) at((p50) wingage) at((p75) wingage) 

Adjusted predictions                              Number of obs   =        376
Model VCE    : EIM

Expression   : Predicted mean compnouc, predict()

1._at        : wingage         =          25 (p25)

2._at        : wingage         =          29 (p50)

3._at        : wingage         =          33 (p75)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |     15.301      0.118   129.43   0.000       15.069      15.533
          2  |     16.969      0.090   188.74   0.000       16.793      17.145
          3  |     18.118      0.090   201.95   0.000       17.942      18.294
------------------------------------------------------------------------------

. 
. meqrlogit compnouc c.wingage ||child:, bin(20) or

Refining starting values: 

Iteration 0:   log likelihood = -834.99187  
Iteration 1:   log likelihood = -826.86355  (not concave)
Iteration 2:   log likelihood = -824.52117  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -824.52117  
Iteration 1:   log likelihood = -823.96019  
Iteration 2:   log likelihood = -823.95862  
Iteration 3:   log likelihood = -823.95862  

Mixed-effects logistic regression               Number of obs      =       376
Binomial trials =  20
Group variable: child                           Number of groups   =       376

                                                Obs per group: min =         1
                                                               avg =       1.0
                                                               max =         1

Integration points =   7                        Wald chi2(1)       =    158.02
Log likelihood = -823.95862                     Prob > chi2        =    0.0000

------------------------------------------------------------------------------
    compnouc | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     wingage |      1.167      0.014    12.57   0.000        1.139       1.195
       _cons |      0.081      0.028    -7.33   0.000        0.041       0.159
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
child: Identity              |
                  var(_cons) |      0.772      0.099         0.601       0.992
------------------------------------------------------------------------------
LR test vs. logistic regression: chibar2(01) =   309.77 Prob>=chibar2 = 0.0000

. margins, at((p25) wingage) at((p50) wingage) at((p75) wingage) predict(mu fixedonly)

Adjusted predictions                              Number of obs   =        376

Expression   : Predicted mean, fixed portion only, predict(mu fixedonly)

1._at        : wingage         =          25 (p25)

2._at        : wingage         =          29 (p50)

3._at        : wingage         =          33 (p75)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |     15.844      0.224    70.64   0.000       15.404      16.283
          2  |     17.519      0.138   127.32   0.000       17.249      17.788
          3  |     18.579      0.119   155.60   0.000       18.345      18.813
------------------------------------------------------------------------------

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