an objective of class "<code><a rd-options="" href="/link/formula?package=ZIM&version=1.1.0" data-mini-rdoc="ZIM::formula">formula</a></code>".

formula

an optional dataframe, list or environment containing the variables in the model.

data

an optional vector specifying a subset of observations to be used in the fitting process.

subset

a function which indicates what should happen when the data contain <code>NA</code>s.

na.action

an optional vector of 'prior weights' to be used in the fitting process.

weights

this can be used to specify a priori known component to be included in the linear predictor during fitting.

offset

control

<code>zim</code> is used to fit zero-inflated models.

regression

Analyze count time series with excess zeros.
Two types of statistical models are supported: Markov regression by Yang et al.
(2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al.
(2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and
parameter-driven models respectively in the time series literature. The functions used for
Markov regression or observation-driven models can also be used to fit ordinary regression models
with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB)
assumption. Besides, the package contains some miscellaneous functions to compute density, distribution,
quantile, and generate random numbers from ZIP and ZINB distributions.

Ming Yang

Zero-Inflated Models (ZIM) for Count Time Series with Excess
Zeros

Gideon Zamba

Joseph Cavanaugh

zim function

an objective of class "<code><a rd-options='' href='formula'>formula</a></code>".

zim: Fitting Zero-Inflated Models

Description

Usage

Arguments

See Also