zeroinfl {pscl} | R Documentation |
Fit zero-inflated regression models for count data via maximum likelihood
zeroinfl(count = y ~ ., x = ~1, z = ~1, data = list(), link = "logit", dist = "poisson", method = "BFGS", trace = FALSE, maxit = 50000, na.action = na.omit)
count |
count being modeled, passed as the left-hand side of a formula ; lowest count must be zero for
zero-inflated models |
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 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 |
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.
an object of class zeroinfl
, i.e., a list with components including
stval |
start values used in optimzation |
par |
Maximum likelihood estimates |
hessian |
Matrix of second derivatives of the log-likelihood
function evaluated at the MLEs; computed numerically by optim |
llh |
value of the log-likelihood function at the MLEs |
y |
vector of counts actually fitted (after any screeing of missing data) |
x |
matrix of covariates used in fitting the count model |
z |
matrix of covariates used in fitting the zero-inflated component |
Simon Jackman <jackman@stanford.edu>
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.
summary.zeroinfl
, predict.zeroinfl
,
hurdle
, glm.nb
Methods are supplied for the generic functions coef
and
logLik
, for objects of class "zeroinfl"
.
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)