[R] Comparison of glm.nb and negbin from the package aod

[sabwo]

I have fitted the faults.data to glm.nb and to the function negbin from the
package aod. The output of both is the following:

summary(glm.nb(n~ll, data=faults))

Call:
glm.nb(formula = n ~ ll, data = faults, init.theta = 8.667407437, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0470  -0.7815  -0.1723   0.4275   2.0896  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -3.7951     1.4577  -2.603  0.00923 ** 
ll            0.9378     0.2280   4.114 3.89e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for Negative Binomial(8.6674) family taken to be 1)

    Null deviance: 50.28  on 31  degrees of freedom
Residual deviance: 30.67  on 30  degrees of freedom
AIC: 181.39

Number of Fisher Scoring iterations: 1


              Theta:  8.67 
          Std. Err.:  4.17 

 2 x log-likelihood:  -175.387 

the output of the function negbin with a global dispersion parameter should
- when i understood it right - yield the same estimates as glm.nb.  it does,
with slightly little differences.

> negbin(n~ll,~1, data=faults)
Negative-binomial model
-----------------------
negbin(formula = n ~ ll, random = ~1, data = faults)

Convergence was obtained after 112 iterations.

Fixed-effect coefficients:
              Estimate Std. Error    z value Pr(> |z|)
(Intercept) -3.795e+00  1.421e+00 -2.671e+00 7.570e-03
ll           9.378e-01  2.221e-01  4.222e+00 2.417e-05

Overdispersion coefficients:
                 Estimate Std. Error   z value   Pr(> z)
phi.(Intercept) 1.154e-01   5.56e-02 2.076e+00 1.895e-02

Log-likelihood statistics
  Log-lik     nbpar   df res.  Deviance       AIC      AICc 
-8.77e+01         3        29 5.209e+01 1.814e+02 1.822e+02

The thing i really dont understand is why there is such a big difference
between the deviances? (glm.nb = 30.67 and negbin=52.09?) Shouldnt they be
nearly the same??

thanks for your help, 
sabine
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