Hi:

You might want to consider hurdle models in the pscl package.

HTH,
Dennis

On Wed, Mar 30, 2011 at 2:41 AM, a11msp <absorbtion@gmail.com> wrote:

> Hello,
>
> I'd like to implement a regression model for extremely zero-inflated
> continuous data using a conditional approach, whereby zeroes are
> modelled as coming from a binary distribution, while non-zero values
> are modelled as log-normal.
>
> So far, I've come across two solutions for this: one, in R, is
> described in the book by Gelman & Hill
> (http://www.amazon.com/dp/052168689X), where they just model zeros and
> non-zeros separately and then bring them together by simulation. I can
> do this, but it makes it difficult to assess the significance of
> regression coefficients wrt to zero and each other.
>
> Another solution I have been pointed at is in SAS:
> http://listserv.uga.edu/cgi-bin/wa?A2=ind0805A&L=sas-l&P=R20779,
> where they use NLMIXED (with only fixed effects) to specify their own
> log-likelihood function.
> I'm wondering if there's any way to do the same in R (lme can't deal
> with this, as far as I'm aware).
>
> Finally, I'm wondering whether anyone has experience with the COZIGAM
> package - does it do something like this?
>
> Many thanks,
> Mikhail
>
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