[R] glm: modelling zeros as binary and non-zeroes as coming from a continuous distribution

[Mikhail Spivakov]

Hi Dennis,
Thanks - these were the first things I tried, but the problem is that
they refuse to work with non-count data...
Mikhail

On Wed, Mar 30, 2011 at 12:56 PM, Dennis Murphy <djmuser at gmail.com> wrote:
> 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 at 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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>
>