Simone Vincenzi <svincenz <at> nemo.unipr.it> writes: > I don't understand from the sentence in the pscl guide "Zero-inflated count > models are a type of two-component mixture model, with a component for zero > counts, and the other component for the positive counts" if: > a)to get true estimate of the relative mean abundance, the model multiply > the relative mean abundance at a site by the probability that the relative > mean abundance at a site is generated through a negative binomial > distribution, as proposed by Lambert (Technometrics, 1992). By using this > kind of mixture model, zeros arise from one or two processes and their > related covariates. > b) we have two independent models, where the first part is a binary outcome > model and the second one is a negative binomial model, assuming that zeros > arise from a single process and set of covariates, as proposed by Dobbie and > Welsh (Austr.N.Z.J.Stat., 2001) > > Thanks > > Simone Vincenzi > PhD student in Ecology, University of Parma, Italy > From your descriptions and a quick look at the two papers you cite, my main conclusion is that I don't really think these are actually different models -- just different descriptions of the same statistical model. Digging into the code in the package and looking at ?zeroinfl shows that the package is fitting a binomial model for the probability of a structural zero and a Poisson or negative binomial for the result otherwise ... you can specify covariates for both models.
As a minor point: both references you cite actually focus on ZI Poisson (not NB) regression models, although Dobbie and Welsh do allow for overdispersion ... hope that helps Ben Bolker ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
