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------------------------
          |  condition  
     time |     0      1
----------+-------------
        0 |     1  1.202
        1 |  2.07   .854
------------------------

The means of observed cell counts are below:

----------------------------
          |    condition    
     time |       0        1
----------+-----------------
        0 | 2.09559  2.64706
        1 | 4.36765  1.91176
----------------------------

These are in the ratios below, which differ from the predicted ratios:

------------------------
          |  condition  
     time |     0      1
----------+-------------
        0 |     1  1.263
        1 | 2.084   .912
------------------------

I had expected that the observed and predicted relative cell frequencies
would be the same for the fitted model.  Can anyone explain why they are not?


Question 2.

I believed that variables which are constant within ID could not be used in
a fixed effects model. Am I wrong?  It seems that I am, as illustrated by
the inclusion of diagnosis (which is constant within a subject).

. xi: xtnbreg  count   diagnosis i.time*condition, i(id) fe irr nolog
i.time            _Itime_0-1          (naturally coded; _Itime_0 omitted)
i.time*condit~n   _ItimXcondi_#       (coded as above)

Conditional FE negative binomial regression     Number of obs      =       544
Group variable (i): id                          Number of groups   =       136

                                                Obs per group: min =         4
                                                               avg =       4.0
                                                               max =         4

                                                Wald chi2(4)       =    172.78
Log likelihood  = -643.28819                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
       count |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   diagnosis |   7.784788   5.952364     2.68   0.007     1.739424     34.8408
    _Itime_1 |   2.072968   .1601387     9.44   0.000     1.781708    2.411841
   condition |   1.180407   .1038162     1.89   0.059     .9935027    1.402473
_ItimXcond~1 |   .3444533   .0414192    -8.86   0.000     .2721301    .4359976
------------------------------------------------------------------------------


Question 3

Can anyone offer advice as to how to assess goodness of fit for xtnbreg ...
, fe? (some models I want to fit are rather more complex than the above
examples).


Any advice or comments will be appreciated

Thanks

John Plummer

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