Kai Ying <yingk <at> iastate.edu> writes: > > Hi, > I want using zero-inflated negative binomial regression model to > classify data(a vector of data), that is I want know each observed value is > more likely belong to the "zero" or "count" distribution(better with > relative probability). My data is some like: > > count site samp > > 12909 1 1 > > 602 1 2 > > 50 1 3 > > 1218 1 4 > > 91291 1 5 > > while "count" is the data with a mixture of "zero" and "non-zero" > distribution I want know, and "site", "samp" are two prediction valuables > with additive effect(but I am not interested in it). > > I have tried the zeroinfl function of pscl package to fit zero-inflated > negative binomial regression. But it can not give you the classification > result of "count". Can anyone help with some indication of how to do it or > other tools that can do this job ??
Not sure, but you may be able to do this by hand. For a predicted mean value mu, overdispersion parameter k, and zero-inflation probability p, the probability p_z of a structural zero is p, while the probability of a sampling zero p_s is (k/(mu+k))^k ; therefore the probability that an observed zero is a structural zero is p_z/(p_s+p_z) ... ______________________________________________ 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.
