Description Usage Arguments Details Value Author(s) References See Also Examples
Fit zeroinflated regression models for count data via maximum likelihood.
1 2 3 4 5 
formula 
symbolic description of the model, see details. 
data, subset, na.action 
arguments controlling formula processing
via 
weights 
optional numeric vector of weights. 
offset 
optional numeric vector with an a priori known component to be included in the linear predictor of the count model. See below for more information on offsets. 
dist 
character specification of count model family (a log link is always used). 
link 
character specification of link function in the binary zeroinflation model (a binomial family is always used). 
control 
a list of control arguments specified via

model, y, x 
logicals. If 
... 
arguments passed to 
Zeroinflated count models are twocomponent mixture models combining a point mass at zero with a proper count distribution. Thus, there are two sources of zeros: zeros may come from both the point mass and from the count component. Usually the count model is a Poisson or negative binomial regression (with log link). The geometric distribution is a special case of the negative binomial with size parameter equal to 1. For modeling the unobserved state (zero vs. count), a binary model is used that captures the probability of zero inflation. in the simplest case only with an intercept but potentially containing regressors. For this zeroinflation model, a binomial model with different links can be used, typically logit or probit.
The formula
can be used to specify both components of the model:
If a formula
of type y ~ x1 + x2
is supplied, then the same
regressors are employed in both components. This is equivalent to
y ~ x1 + x2  x1 + x2
. Of course, a different set of regressors
could be specified for the count and zeroinflation component, e.g.,
y ~ x1 + x2  z1 + z2 + z3
giving the count data model y ~ x1 + x2
conditional on (
) the zeroinflation model y ~ z1 + z2 + z3
.
A simple inflation model where all zero counts have the same
probability of belonging to the zero component can by specified by the formula
y ~ x1 + x2  1
.
Offsets can be specified in both components of the model pertaining to count and
zeroinflation model: y ~ x1 + offset(x2)  z1 + z2 + offset(z3)
, where
x2
is used as an offset (i.e., with coefficient fixed to 1) in the
count component and z3
analogously in the zeroinflation component. By the rule
stated above y ~ x1 + offset(x2)
is expanded to
y ~ x1 + offset(x2)  x1 + offset(x2)
. Instead of using the
offset()
wrapper within the formula
, the offset
argument
can also be employed which sets an offset only for the count model. Thus,
formula = y ~ x1
and offset = x2
is equivalent to
formula = y ~ x1 + offset(x2)  x1
.
All parameters are estimated by maximum likelihood using optim
,
with control options set in zeroinfl.control
.
Starting values can be supplied, estimated by the EM (expectation maximization)
algorithm, or by glm.fit
(the default). Standard errors
are derived numerically using the Hessian matrix returned by optim
.
See zeroinfl.control
for details.
The returned fitted model object is of class "zeroinfl"
and is similar
to fitted "glm"
objects. For elements such as "coefficients"
or
"terms"
a list is returned with elements for the zero and count component,
respectively. For details see below.
A set of standard extractor functions for fitted model objects is available for
objects of class "zeroinfl"
, including methods to the generic functions
print
, summary
, coef
,
vcov
, logLik
, residuals
,
predict
, fitted
, terms
,
model.matrix
. See predict.zeroinfl
for more details
on all methods.
An object of class "zeroinfl"
, i.e., a list with components including
coefficients 
a list with elements 
residuals 
a vector of raw residuals (observed  fitted), 
fitted.values 
a vector of fitted means, 
optim 
a list with the output from the 
control 
the control arguments passed to the 
start 
the starting values for the parameters passed to the 
weights 
the case weights used, 
offset 
a list with elements 
n 
number of observations (with weights > 0), 
df.null 
residual degrees of freedom for the null model (= 
df.residual 
residual degrees of freedom for fitted model, 
terms 
a list with elements 
theta 
estimate of the additional theta parameter of the negative binomial model (if a negative binomial regression is used), 
SE.logtheta 
standard error for log(theta), 
loglik 
loglikelihood of the fitted model, 
vcov 
covariance matrix of all coefficients in the model (derived from the
Hessian of the 
dist 
character string describing the count distribution used, 
link 
character string describing the link of the zeroinflation model, 
linkinv 
the inverse link function corresponding to 
converged 
logical indicating successful convergence of 
call 
the original function call, 
formula 
the original formula, 
levels 
levels of the categorical regressors, 
contrasts 
a list with elements 
model 
the full model frame (if 
y 
the response count vector (if 
x 
a list with elements 
Achim Zeileis <Achim.Zeileis@Rproject.org>
Cameron, A. Colin and Pravin K. Trevedi. 1998. Regression Analysis of Count Data. New York: Cambridge University Press.
Cameron, A. Colin and Pravin K. Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge: Cambridge University Press.
Lambert, Diane. 1992. “ZeroInflated Poisson Regression, with an Application to Defects in Manufacturing.” Technometrics. 34(1):114
Zeileis, Achim, Christian Kleiber and Simon Jackman 2008. “Regression Models for Count Data in R.” Journal of Statistical Software, 27(8). URL http://www.jstatsoft.org/v27/i08/.
zeroinfl.control
, glm
,
glm.fit
, glm.nb
,
hurdle
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## data
data("bioChemists", package = "pscl")
## without inflation
## ("art ~ ." is "art ~ fem + mar + kid5 + phd + ment")
fm_pois < glm(art ~ ., data = bioChemists, family = poisson)
fm_qpois < glm(art ~ ., data = bioChemists, family = quasipoisson)
fm_nb < MASS::glm.nb(art ~ ., data = bioChemists)
## with simple inflation (no regressors for zero component)
fm_zip < zeroinfl(art ~ .  1, data = bioChemists)
fm_zinb < zeroinfl(art ~ .  1, data = bioChemists, dist = "negbin")
## inflation with regressors
## ("art ~ .  ." is "art ~ fem + mar + kid5 + phd + ment  fem + mar + kid5 + phd + ment")
fm_zip2 < zeroinfl(art ~ .  ., data = bioChemists)
fm_zinb2 < zeroinfl(art ~ .  ., data = bioChemists, dist = "negbin")

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