Thanks, Mollie!  That's a very good (in retrospect, obvious) point I had
overlooked about needing effective samples to be non-zero.  I've had a look
at the paper that Ben cited, and I appreciate your tip regarding the
scale() function.  Very handy!

- Jason

On Thu, May 3, 2012 at 3:55 PM, Mollie Brooks <mbrooks@ufl.edu> wrote:

>
>
> Your effective sample size (what determines how many parameters you can
> fit) is more like the number of non-zero observations you have. Then you
> can estimate about one tenth that many parameters. So for the model you're
> trying to fit, you would need 50 to 80 non-zero observations.
>
> I would first try fitting a zero-inflated Poisson because they're less
> finicky than negative binomial. It's the negative binomial part of your
> models that seem to be broken.
>
> As Ben said on the mixed models list, it could help to center and scale
> the continuous predictors using scale().
> cheers,
> Mollie
>
> > [cut]
>


> > Reading other posts, I know that my values are all >= 0, and there are
> > no missing values that could be causing this, but there ARE lots of
> > zeroes.  My sample size is decent (about 35 observations per
> > treatment, per elevation).
> > So my questions are essentially as
> > follows:
> >
> > 1) Why am I getting all these NaNs, and why can’t I run the full
> > (saturated) model, including continuous as well as categorical
> > predictors and interactions?  How can I begin the process of model
> > simplification if I can’t get the full model to run?
> >
> > 2) If this is a result of the many zeroes in my data set, is there a
> > way to work around this?
> >
> > Any advice would be greatly appreciated.
> >
> > Jason Straka
> > University of Victoria
>
>

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