This is equivalent to y ~ x1 + x2 | x1 + x2. Of course, a different set of regressors could be specified for the count and zero-inflation component, e.g.
y ~ x1 + x2 | z1 + z2 + z3 giving the count data model y ~ x1 + x2 conditional on ( |) the zero-inflation model y ~ z1 + z2 + z3 .
zeroinfl Examples # NOT RUN { data(bioChemists, package = pscl) ## default start values fm1 <- zeroinfl(art ~ .data = bioChemists) ## use EM algorithm for start values fm2 <- zeroinfl(art ~ .data = bioChemists, EM = TRUE) ## user-supplied start values fm3 <- zeroinfl(art ~ .data = bioChemists, start = list(count = c(0.7, -0.2, 0.1, -0.2, 0, 0), zero = -1.7)) # }, zeroinfl. Examples # NOT RUN { data(bioChemists, package = pscl) fm_zip <- zeroinfl(art ~ .data = bioChemists) plot(residuals(fm_zip) ~ fitted(fm_zip)) coef(fm_zip) coef(fm_zip, model = count) summary(fm_zip) logLik(fm_zip) # }, Zero-Inflated Poisson Regression | R Data Analysis Examples, Zero-Inflated Negative Binomial Regression | R Data ...zeroinfl: Zero-inflated Count Data Regression in countreg ...Regression Models for Count Data in R, 12/10/2018 · Of course, a different set of regressors could be specified for the count and zero-inflation component, e.g.y ~ x1 + x2 | z1 + z2 + z3 giving the count data model y ~ x1 + x2 conditional on ( |) the zero-inflation model y ~ z1 + z2 + z3 .be. zeroinfl (object, data, dist=c(poisson, negbin, geometric), alpha=0.05, trace=FALSE), (3) ZERO-INFLATED POISSON REGRESSION in R > m1 zeroinfl (panic~Age+gender+married+divorce|gender, data=nlaas) > summary(m1) Call: zeroinfl (formula = panic ~ Age + gender + married + divorce | gender, data = nlaas) Pearson residuals: Min 1Q Median 3Q Max -0.4001 -0.3939 -0.2709 -0.2666 6.3065, For your example , if you do: zeroinfl (n ~ Plot + var1+var2 + var1*var2|1, data = dat) You are modeling the zero part to be missing at random with a probability that is constant. If you believe your zero values are also dependent on the other variables, you can do: zeroinfl (n ~ Plot + var1+var2 + var1*var2|Plot + var1+var2 + var1*var2, data = dat), and zeroinfl () in the pscl package (Jackman2008), available from the Comprehensive R Archive Network (CRAN) at http:// CRAN.R-project.org /package=pscl. The design of both modeling functions as well as the methods operating on the associated tted model objects follows that of the base R functionality so that the new software integrates easily into
