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.
