
Thank you very much for the answer, it helped me a lot!

Regards,

Sabine Woschitz


-------- Original-Nachricht --------
> Datum: Sat, 12 Feb 2011 01:04:46 -0800 (PST)
> Von: "Matthieu Lesnoff [via R]" <ml-node+3302494-847497758-212949@n4.nabble.com>
> An: sabwo <sabsiw@gmx.at>
> Betreff: Re: Comparison of glm.nb and negbin from the package aod

> 
> 
> Dear Sabine
> 
> In negbin(aod), the deviance is calculated by:
> 
> # full model
> logL.max <- sum(dpois(x = y, lambda = y, log = TRUE))
> # fitted model
> logL <- -res$value
> dev <- -2 * (logL - logL.max)
> 
> (the log-Lik contain all the constants)
> 
> As Ben Bolker said, whatever the formula used for deviance, differences 
> between deviances of two models should be the same
> 
> Regards
> 
> -- 
> ------------------
> Matthieu Lesnoff
> 
> On 10/02/2011 18:00, sabwo wrote:
> >
> > 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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