------------------------
| 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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