I notice there is a R code for a Zero-inflated Poisson/NB process on the Stanford Political Science Computational Lab (Prof. Simon Jackman) web page. If I were wanting to do a one-inflated model, I would start with that because, at least to my eye, it is very easy to follow. Mind you, I did not try this myself, but I bet you could make it go. In the file zeroinfl.r, look at the function:
zeroinflNegBin <- function(parms){
it is pretty clear you'd have to supply a probability model for the outcomes valued 1 and then fit them into the overall likelihood.
pj
http://pscl.stanford.edu/content.html Peter Flom wrote:
Hello
I am interested in Poisson or (ideally) Negative Binomial regression with an inflated number of 1 responses
I have seen JK Lindsey's fmr function in the gnlm library, which fits zero inflated Poisson (ZIP) or zero inflated negative binomial regression, but the help file states that for ' Poisson or related distributions the mixture involves the zero category'.
I had thought of perhaps subtracting 1 from all the counts and then fitting the ZIP or ZINB models, and then adding 1, but am not sure if this is legitimate, or if there is some better method.
Contextual details: The dependent variable is number of primary sexual partners in the last year. The independent variables include a) Being married or in a committed relationship b) using hard drugs c) sex d) age
N is c. 500
Not surprisingly, there are a large number of 1 responses, especially for those who are married or in a relationship. More surprisingly, the mean number of partners is the same (1.05 vs. 1.02) for people in and not in relationships, but the variances are very different, mostly because those in a relationhsip are much more likely to say exactly 1.
Thanks in advance
Peter
Peter L. Flom, PhD
Assistant Director, Statistics and Data Analysis Core
Center for Drug Use and HIV Research
--
Paul E. Johnson email: [EMAIL PROTECTED]
Dept. of Political Science http://lark.cc.ku.edu/~pauljohn
1541 Lilac Lane, Rm 504 University of Kansas Office: (785) 864-9086
Lawrence, Kansas 66044-3177 FAX: (785) 864-5700
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