[R] package MASS - MLE of negative binomial distributions

[Rik Verdonck]

That makes sense indeed, but it also raises further questions to me if you don't mind:
- Is there a good reason why the default assumption would be a constant overdispersion?  Is it because you lose fewer degrees of freedom which would give you more power in comparing models?
- Is there a way to tell glm.nb to fit the thetas separately per group?
- Is there a way to fix theta (i.e. feed it a value) and only make an estimate of the expected value?

Many thanks!
Rik


________________________________________
Van: peter dalgaard [pdalgd at gmail.com]
Verzonden: donderdag 10 december 2015 16:53
Aan: Rik Verdonck
CC: r-help at r-project.org
Onderwerp: Re: [R] package MASS - MLE of negative binomial distributions

glm.nb fits 6 mean parameters plus 1 theta. 6 x fitdistr fits two parameters each.

-pd

On 10 Dec 2015, at 16:21 , Rik Verdonck <Rik.Verdonck at bio.kuleuven.be> wrote:

> Dear list,
>
>
>
> I have a question about the exact estimate of the maximum likelihood for a negative binomial fit. I'm trying to approach this in two different ways: the first one is a fit using the glm.nb method, and the second one is a fit using the fitdistr function for each condition separately, where I add up all log likelihoods. These two methods do not yield the same values for the log likelihood of the fit. They do yield the same log likelihood if all data are one group (no summation), so I assume I'm doing something wrong when I sum up log likelihoods. Am I not "allowed" to do this?
>
>
> Example code:
> library(MASS)
> x<-c(601,619,637,609,594,499,494,507,477,450,400,367,428,359,400,276,260,262,304,342,216,189,152,231,200,104,85,85,85,112)
> groups<-as.factor(c(rep("dist1",5),rep("dist2",5),rep("dist3",5),rep("dist4",5),rep("dist5",5),rep("dist6",5)))
>
> glm.nb(x~groups)$twologlik
>
> logliks<-NULL
> for(group in levels(groups))
> {
>       NBfit<-fitdistr(x[groups==group],"Negative Binomial")
>       logliks<-c(logliks,NBfit$loglik)
>       rm(NBfit)
> }
>
> sum(logliks)*2
>
>
> Many thanks!
> Rik
>
>
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--
Peter Dalgaard, Professor,
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