pscl (version 0.5)

hurdle: Fit hurdle models to count data

Description

Estimate a hurdle regression model for count data model via maximum likelihood

Usage

hurdle(count = y ~ ., x = ~1, z = ~1, data = list(), link = "logit",
        dist = "poisson", method = "BFGS", trace = FALSE,
        maxit = 50000, na.action = na.omit)

Arguments

count

count being modeled, passed as the left-hand side of a link{formula}; lowest count must be zero

x

right-hand side of a formula giving covariates for the count part of the model

z

right-hand side of a formula giving covariates for the hurdle part of the model

data

a data frame

link

link function for zero-inflated part of the model (choices are logit or probit)

dist

count model, "poisson" (default) or "negbin"

method

method for maximizing the log-likelihood function, only "BFGS" and "Nelder-Mead" are supported

trace

logical, if TRUE, display progress of maximization

maxit

maximum number of iterations in maximization

na.action

method for handling missing data, default is na.omit

Value

• An object of class "hurdle"

See Also

zeroinfl, summary.hurdle

Examples

data(bioChemists)
hp <- hurdle(count=art ~ .,
              x = ~ fem + mar + kid5 + phd + ment,
              z = ~ fem + mar + kid5 + phd + ment,
              data=bioChemists,trace=TRUE)
summary(hp)

hnb <- hurdle(count=art ~ .,
              x = ~ fem + mar + kid5 + phd + ment,
              z = ~ fem + mar + kid5 + phd + ment,
              dist="negbin",
              data=bioChemists,trace=TRUE)
summary(hnb)