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st: estimation using gllamm, oprobit model fails to converge


From   Frank Gallo <fjgallo@mac.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: estimation using gllamm, oprobit model fails to converge
Date   Mon, 10 Aug 2009 18:57:28 -0400

Hi All,

I used -gllamm- to run a Random Intercepts-Only Model. Below is the output. The DV is ordinal, and believed to have a continuous latent continuum. I am teaching myself multilevel modeling, Stata, and the _gllamm- command. I am using Stata Version 11. Would the below failure to converge suggest that there is little variability between j groups on the DV? "or" Did I do something wrong in the model specification? I found that this model - xtmixed pforce || pd:, mle variance - converged and yielded a significant between-group difference that suggested groups mattered. I would greatly appreciate any guidance and resources. I have been using Rabe-Hesketh & Skrondal's (2008) book for Stata. Thank you.

Best,
Frank


. gllamm pforce, i(pd) nip(12) link(oprobit) adapt trace

General model information
------------------------------------------------------------------------------

dependent variable:         pforce
ordinal responses:         oprobit
equations for fixed effects
                           _cut11:  _cons
                           _cut12:  _cons
                           _cut13:  _cons
                           _cut14:  _cons
                           _cut15:  _cons
                           _cut16:  _cons
                           _cut17:  _cons
                           _cut18:  _cons
                           _cut19:  _cons
                           _cut110:  _cons
                           _cut111:  _cons
                           _cut112:  _cons
                           _cut113:  _cons
                           _cut114:  _cons
                           _cut115:  _cons
                           _cut116:  _cons
                           _cut117:  _cons
                           _cut118:  _cons
                           _cut119:  _cons
                           _cut120:  _cons
                           _cut121:  _cons
                           _cut122:  _cons


Random effects information for 2 level model
------------------------------------------------------------------------------



***level 2 (pd) equation(s):

   standard deviation of random effect
   pd1: _cons

number of level 1 units = 3300
number of level 2 units = 16

Initial values for fixed effects


Iteration 0:   log likelihood = -2735.2811

Ordered probit estimates Number of obs = 3300 LR chi2(0) = 0.00 Prob > chi2 = . Log likelihood = -2735.2811 Pseudo R2 = 0.0000

------------------------------------------------------------------------------
pforce | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------------- +---------------------------------------------------------------- ------------- +----------------------------------------------------------------
       _cut1 |  -1.583387    .035338          (Ancillary parameters)
       _cut2 |   1.196465   .0285592
       _cut3 |    1.19802   .0285798
       _cut4 |   1.202704   .0286423
       _cut5 |   1.269557   .0295796
       _cut6 |   1.271259   .0296046
       _cut7 |   1.276389   .0296804
       _cut8 |   1.464599   .0328633
       _cut9 |   1.466823    .032906
      _cut10 |   1.524945   .0340702
      _cut11 |   1.529823   .0341722
      _cut12 |   1.674974   .0375413
      _cut13 |   1.678071   .0376208
      _cut14 |   1.684313    .037782
      _cut15 |   1.806059   .0412223
      _cut16 |   1.809953   .0413422
      _cut17 |   1.947163   .0460173
      _cut18 |   2.262989   .0610329
      _cut19 |   2.349713   .0665442
      _cut20 |   2.361894   .0673782
      _cut21 |   3.236012   .2017793
      _cut22 |   3.428888   .2713744
------------------------------------------------------------------------------
------------------------------------------------------------------------------


start running on 10 Aug 2009 at 17:55:00

Running adaptive quadrature
------------------------------------------------------------------------------
Iteration 0 of adaptive quadrature:
Initial parameters:

_cut11: _cut12: _cut13: _cut14: _cut15: _cut16: _cut17: _cut18: _cut19: _cut110: _cut111: _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons y1 -1.583387 1.196465 1.19802 1.202704 1.269557 1.271259 1.276389 1.464599 1.466823 1.524945 1.529823

_cut112: _cut113: _cut114: _cut115: _cut116: _cut117: _cut118: _cut119: _cut120: _cut121: _cut122: _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons _cons y1 1.674974 1.678071 1.684313 1.806059 1.809953 1.947163 2.262989 2.349713 2.361894 3.236012 3.428888

          pd1:
        _cons
y1         .5

Updated log likelihood:
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
         0          0          0          0          0          0
0 0 0 0 0 Convergence not achieved: try with more quadrature points
finish running on 10 Aug 2009 at 17:55:31

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