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st: -nbreg- not working properly?
Dear count data analysts,
I am puzzled by the following. If I'm not mistaken, -nbreg- (with defaults)
should produce the same results as -glm- with family(nbinomial). But in my
analysis, this is not the case, they are very different. -nbreg- is giving
me results that are almost identical to -poisson- (and -glm- with
family(poisson)). I have pasted the results of -nbreg-, followed by -glm-
family(nbinomial), followed by -poisson-. Note the extremely low alpha
in -nbreg-, which explains why it looks like the -poisson- results. This
would suggest that the data are actually conditionally poisson, but this
would not explain the difference to the -glm- result. I think its unlikely
that my data are actually conditionally poisson, so I tend to believe
the -glm- result over nbreg (though I'm unsure how retreive alpha
from -glm-). Your advice would be appreciated. Am I doing something silly?
Thanks in advance,
Peter.
nbreg pbs01 yr1 yr2 hcard agecont agecont2 sex y y2 ms hchbp hcchol
diabcond asthcond sa
> h2 sah3 sah4 sah5 [pweight=weight2] if group == 1, cluster(randomid2)
Fitting Poisson model:
Iteration 0: log pseudolikelihood = -420.43515
Iteration 1: log pseudolikelihood = -419.99682
Iteration 2: log pseudolikelihood = -419.99619
Iteration 3: log pseudolikelihood = -419.99619
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -550.19552
Iteration 1: log pseudolikelihood = -548.65093
Iteration 2: log pseudolikelihood = -548.64639
Iteration 3: log pseudolikelihood = -548.64639
Fitting full model:
Iteration 0: log pseudolikelihood = -467.09114
Iteration 1: log pseudolikelihood = -437.76074
Iteration 2: log pseudolikelihood = -422.73894
Iteration 3: log pseudolikelihood = -420.16013
Iteration 4: log pseudolikelihood = -420.04043
Iteration 5: log pseudolikelihood = -420.00748
Iteration 6: log pseudolikelihood = -419.99862
Iteration 7: log pseudolikelihood = -419.99672
Iteration 8: log pseudolikelihood = -419.99631
Iteration 9: log pseudolikelihood = -419.99622
Iteration 10: log pseudolikelihood = -419.9962
Negative binomial regression Number of obs =
792
Dispersion = mean Wald chi2(17) =
512.24
Log pseudolikelihood = -419.9962 Prob > chi2 =
0.0000
(Std. Err. adjusted for 715 clusters in
randomid2)
----------------------------------------------------------------------------
--
| Robust
pbs01 | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
yr1 | -.2793689 .109702 -2.55
011 -.4943808 -.064357
yr2 | -.4742796 .1160246 -4.09
0.000 -.7016836 -.2468756
hcard | .150871 .114099 1.32 0.186 -.0727589
3745009
agecont | .2116223 .5903352 0.36 0.720 -.9454134
1.368658
agecont2 | -.0012236 .0040618 -0.30 0.763 -.0091846
0067374
sex | -.0313338 .0899033 -0.35 0.727 -.207541
1448734
y | .0442019 .0684746 0.65 0.519 -.0900059
1784098
y2 | -.0028061 .0056018 -0.50 0.616 -.0137854
0081732
ms | -.0114624 .0921729 -0.12 0.901 -.192118
1691932
hchbp | 1.107994 .1076378 10.29 0.000 .8970276
1.31896
hcchol | .5759967 .0943939 6.10 0.000 .3909881
7610053
diabcond | .5188646 .1149093 4.52 0.000 .2936464
7440828
asthcond | .8383796 .1282918 6.53 0.000 .5869324
1.089827
sah2 | .2328374 .1673571 1.39 0.164 -.0951764
5608512
sah3 | .3755013 .1602553 2.34 0.019 .0614067
6895959
sah4 | .6246108 .1723855 3.62 0.000 .2867414
9624801
sah5 | .8188331 .2241906 3.65 0.000 .3794275
1.258239
_cons | -10.15595 21.43698 -0.47 0.636 -52.17166
31.85977
-------------+--------------------------------------------------------------
--
/lnalpha | -15.17458
8177613 -16.77736 -13.57179
-------------+--------------------------------------------------------------
--
alpha | 2.57e-07 2.10e-07 5.17e-08
1.28e-06
----------------------------------------------------------------------------
--
glm pbs01 yr1 yr2 hcard agecont agecont2 sex y y2 ms hchbp hcchol diabcond
asthcond sah2
> sah3 sah4 sah5 [pweight=weight2] if group == 1, family(nbinomial)
cluster(randomid2)
Iteration 0: log pseudolikelihood = -464.94184
Iteration 1: log pseudolikelihood = -463.24718
Iteration 2: log pseudolikelihood = -463.24266
Iteration 3: log pseudolikelihood = -463.24266
Generalized linear models No. of obs =
792
Optimization : ML Residual df =
774
Scale parameter =
1
Deviance = 194.0741705 (1/df) Deviance =
2507418
Pearson = 204.4292246 (1/df) Pearson =
2641204
Variance function: V(u) = u+(1)u^2 [Neg. Binomial]
Link function : g(u) = ln(u) [Log]
AIC =
1.215259
Log pseudolikelihood = -463.2426575 BIC
= -4972.036
(Std. Err. adjusted for 715 clusters in
randomid2)
----------------------------------------------------------------------------
--
| Robust
pbs01 | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
yr1 | -.3866717 .1256955 -3.08
002 -.6330304 -.140313
yr2 | -.5355483 .1226364 -4.37
0.000 -.7759113 -.2951853
hcard | .1641242 .1157894 1.42 0.156 -.0628188
3910672
agecont | .3145927 .6561289 0.48 0.632 -.9713963
1.600582
agecont2 | -.0019006 .0045216 -0.42 0.674 -.0107627
0069614
sex | -.0199701 .0940797 -0.21 0.832 -.2043629
1644227
y | -.006733 .0834051 -0.08 0.936 -.170204
156738
y2 | .0011065 .0067524 0.16 0.870 -.0121279
014341
ms | -.0182507 .1053028 -0.17 0.862 -.2246403
1881389
hchbp | 1.344269 .1090522 12.33 0.000 1.13053
1.558007
hcchol | .749958 .0989466 7.58 0.000 .5560263
9438898
diabcond | .6907447 .1526588 4.52 0.000 .391539
9899504
asthcond | 1.129922 .1577985 7.16 0.000 .8206428
1.439201
sah2 | .222886 .1714293 1.30 0.194 -.1131092
5588813
sah3 | .3962243 .1653541 2.40 0.017 .0721362
7203125
sah4 | .7484651 .1680427 4.45 0.000 .4191075
1.077823
sah5 | 1.140498 .269097 4.24 0.000 .6130776
1.667918
_cons | -14.15646 23.73327 -0.60 0.551 -60.6728
32.35989
----------------------------------------------------------------------------
--
poisson pbs01 yr1 yr2 hcard agecont agecont2 sex y y2 ms hchbp hcchol
diabcond asthcond
> sah2 sah3 sah4 sah5 [pweight=weight2] if group == 1, cluster(randomid2)
Iteration 0: log pseudolikelihood = -420.43515
Iteration 1: log pseudolikelihood = -419.99682
Iteration 2: log pseudolikelihood = -419.99619
Iteration 3: log pseudolikelihood = -419.99619
Poisson regression Number of obs =
792
Wald chi2(17) =
512.26
Log pseudolikelihood = -419.99619 Prob > chi2 =
0.0000
(Std. Err. adjusted for 715 clusters in
randomid2)
----------------------------------------------------------------------------
--
| Robust
pbs01 | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
yr1 | -.2793343 .1097001 -2.55
0.011 -.4943426 -.0643259
yr2 | -.4742559 .1160211 -4.09
0.000 -.701653 -.2468588
hcard | .1508732 .1140992 1.32 0.186 -.0727571
3745034
agecont | .2116157 .5903103 0.36 0.720 -.9453713
1.368603
agecont2 | -.0012235 .0040616 -0.30 0.763 -.0091842
0067371
sex | -.0313402 .0898996 -0.35 0.727 -.2075402
1448598
y | .0442089 .0684746 0.65 0.519 -.0899989
1784166
y2 | -.0028066 .0056017 -0.50 0.616 -.0137858
0081726
ms | -.0114684 .0921717 -0.12 0.901 -.1921217
1691849
hchbp | 1.10793 .1076391 10.29 0.000 .8969617
1.318899
hcchol | .5759237 .0943921 6.10 0.000 .3909184
7609289
diabcond | .5187948 .1149089 4.51 0.000 .2935775
7440121
asthcond | .8382751 .1282901 6.53 0.000 .5868311
1.089719
sah2 | .2328442 .1673569 1.39 0.164 -.0951693
5608578
sah3 | .3755066 .1602558 2.34 0.019 .0614111
6896021
sah4 | .6245785 .1723873 3.62 0.000 .2867056
9624514
sah5 | .8187356 .2241965 3.65 0.000 .3793186
1.258153
_cons | -10.15561 21.43606 -0.47 0.636 -52.16951
31.8583
----------------------------------------------------------------------------
--
Peter Siminski
PhD Student
School of Economics / Social Policy Research Centre (SPRC)
University of New South Wales
Ph: 0425223257
[email protected]
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