This request is related to the following post from last year: https://stat.ethz.ch/pipermail/r-help/2011-June/279752.html
After reading the thread, the idea is still not clear. I have fitted a model using HURDLE from the PSCL package. I am trying to get marginal effects / slopes by multiplying the coefficients by the mean of the marginal effects (I think this is right). To my understanding, this will require a mean for the binary probability model and a mean for the truncated Poisson count model. My guess is that I would use mean(predict( MODELNAME, type = "XXX")) where MODELNAME is the hurdle model and XXX is either RESPONSE, COUNT, or ZERO. Assuming the above is right (correct me if it isn't), my questions are: 1. What XXX gives me the mean of the marginal effects for the binomial probability model? 2. What XXX gives me the mean of the marginal effects for the count model? Judging from my results, I would guess the answer to question 1 is COUNT, except max(predict(MODELNAME, type= "count")) returns 4.5 and I expected it to be less than 1. I would also have expected COUNT to match up with the truncated Poisson count model. What is the intuition here? Also, when I try XXX = PROB, I get the following error: Error in matrix(NA, nrow = length(mu), ncol = nUnique) : too many elements specified So maybe there are other problems. ______________________________________________ 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.
