Hi: You might want to consider hurdle models in the pscl package.
HTH, Dennis On Wed, Mar 30, 2011 at 2:41 AM, a11msp <absorbt...@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 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
