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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