zim.fit

design matrix for log-linear part.

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

weights

offset

control arguments from <code><a rd-options="" href="/link/zim.control?package=ZIM&version=1.1.0" data-mini-rdoc="ZIM::zim.control">zim.control</a></code>.

control

<code><a rd-options="" href="/link/zim.fit?package=ZIM&version=1.1.0" data-mini-rdoc="ZIM::zim.fit">zim.fit</a></code> is the basic computing engine called by <code><a rd-options="" href="/link/zim?package=ZIM&version=1.1.0" data-mini-rdoc="ZIM::zim">zim</a></code> used to fit 
zero-inflated models. This should usually not be used directly unless by experienced users.

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.fit function

control arguments from <code><a rd-options='' href='zim.control'>zim.control</a></code>.

<code><a rd-options='' href='zim.fit'>zim.fit</a></code> is the basic computing engine called by <code><a rd-options='' href='zim'>zim</a></code> used to fit 
zero-inflated models. This should usually not be used directly unless by experienced users.

zim.fit: Fitter Function for Zero-Inflated Models

Description

Usage

Arguments

See Also