Hello, It might be more of a statistical question than an R question.
I was reading http://cran.r-project.org/web/packages/pscl/vignettes/countreg.pdf, and I was wondering why the following functions were used to compare zero counts (observed and predicted), instead of just using hist(fitted(fm_pois),plot=FALSE), then the counts of the bin of 0 (which is simply count of 0 from fitted values). This is because I get nice zero counts using the following functions, but my fitted (predicted) values are rather off, so I was wondering what the following comparison means as supposed to the fitted values. R> round(c("Obs" = sum(dt$ofp < 1), + "ML-Pois" = sum(dpois(0, fitted(fm_pois))), + "NB" = sum(dnbinom(0, mu = fitted(fm_nbin), size = fm_nbin$theta)), + "NB-Hurdle" = sum(predict(fm_hurdle, type = "prob")[,1]), + "ZINB" = sum(predict(fm_zinb, type = "prob")[,1]))) Obs ML-Pois NB NB-Hurdle ZINB 683 47 608 683 709 Any comments would be appreciated. Thank you in advance. Sincerely, Jamie -- View this message in context: http://r.789695.n4.nabble.com/count-regression-zero-count-comparison-tp3810907p3810907.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
