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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