zeroinfl: Zero-inflated regression models for count data

Description

Fit zero-inflated regression models for count data via maximum likelihood

Usage

zeroinfl(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 formula; lowest count must be zero for zero-inflated models

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

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

data

a data frame

link

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

dist

type of 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 zeroinfl, i.e., a list with components including
stvalstart values used in optimzation
parMaximum likelihood estimates
hessianMatrix of second derivatives of the log-likelihood function evaluated at the MLEs; computed numerically by optim
llhvalue of the log-likelihood function at the MLEs
yvector of counts actually fitted (after any screeing of missing data)
xmatrix of covariates used in fitting the count model
zmatrix of covariates used in fitting the zero-inflated component

Details

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. Poisson or negative-binomial models are used for the count component of the model; logit or probit is typically used to model the probability of a zero-count. optim is used to find maximum likelihood estimates and to compute a Hessian matrix after convergence.

References

Lambert, Diane. 1992. "Zero-Inflated Poisson Regression, With an Application to Defects in Manufacturing." Technometrics.V34(1):1-14

Cameron, A. Colin and Pravin K. Trevedi. 1998. Regression analysis of count data. New York: Cambridge University Press.

Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Number 7 in Advanced Quantitative Techniques in the Social Sciences. Thousand Oaks, California: Sage.

Examples

Run this code

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

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

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