[R-sig-ME] Problems fitting a hurdle model using glmmADMB

[David Duffy]

On Fri, 27 Jun 2014, Shawn O'Neil wrote:

> I've explored my data some more and have an update to the above message. I
> think my model for the non-zero counts is underfitting because the
> zero-truncated negative binomial distribution is not a great representation
> of the data. My count data for y>0 are as following:
>
>> summary(as.factor(dat.nz$Count))
>  1   2   3   4   5   6   7   8  10  11  12
> 172  39  18  14   4   8   2   1   1   1   1
>
> The zero-truncated negative binomial family does not seem to fit for this
> many ones and the corresponding drop-off to lower values. Thus, I suppose I
> shouldn't use this family of models for the 2nd part of the hurdle model
> that I'm trying to fit. But what are the alternatives? I have thought about
> quantile regression and am searching for non-parametric methods, but I am
> not sure if it is acceptable to split up the model in this way.

Ordinal or threshold models eg ordinal or lavaan packages.  The gaussian 
threshold model allows you to check model assumptions to some extent eg 
the chisq test for bivariate normality from polycor in the polychor 
package (which you can do on all two-way margins).

Cheers, David Duffy.

| David Duffy (MBBS PhD)
| email: David.Duffy at qimrberghofer.edu.au  ph: INT+61+7+3362-0217 fax: -0101
| Genetic Epidemiology, QIMR Berghofer Institute of Medical Research
| 300 Herston Rd, Brisbane, Queensland 4006, Australia  GPG 4D0B994A