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fami
2012 Oct 22
1
glm.nb - theta, dispersion, and errors
...5 0.4
S9 2.120 0.283 1.2
So I thought using maximum likelihood Poisson models would provide wrong
results, and that using negative binomial models would be the best way to
go.
3 models cause the following problem (the ones that are not overdispersed):
> m2.nb <- glm.nb(t0s2 ~ Sex + HisDep + FamHis + ZEFE + ZNeuro, data=data)
>Error in while ((it <- it + 1) < limit && abs(del) > eps) { :
>missing value where TRUE/FALSE needed
What happens is that, looking at theta iterations, values become too large,
and thereby infinite, and thereby NaN.
This is an example for model...