pAIC

Extract the pseudo Akaike information criterion (pAIC)
for objects of <code>mcglm</code> class.

Fitting multivariate covariance generalized linear
models (McGLMs) to data. McGLM is a general framework for non-normal
multivariate data analysis, designed to handle multivariate response
variables, along with a wide range of temporal and spatial correlation
structures defined in terms of a covariance link function combined
with a matrix linear predictor involving known matrices.
The models take non-normality into account in the conventional way
by means of a variance function, and the mean structure is modelled
by means of a link function and a linear predictor.
The models are fitted using an efficient Newton scoring algorithm
based on quasi-likelihood and Pearson estimating functions, using
only second-moment assumptions. This provides a unified approach to
a wide variety of different types of response variables and covariance
structures, including multivariate extensions of repeated measures,
time series, longitudinal, spatial and spatio-temporal structures.
The package offers a user-friendly interface for fitting McGLMs
similar to the glm() R function.
See Bonat (2018) <doi:10.18637/jss.v084.i04>, for more information
and examples.

Wagner Hugo Bonat

mcglm

Multivariate Covariance Generalized Linear Models

pAIC function

<dl><dt>object</dt>
<dd>an object or a list of objects representing a model
of <code>mcglm</code> class.</dd>
<dt>verbose</dt>
<dd>logical. Print or not the pAIC value.</dd></dl>

Arguments

Wagner Hugo Bonat, <a href="/link/wbonat%40ufpr.br?package=mcglm&version=0.8.0" data-mini-rdoc="mcglm::wbonat@ufpr.br">wbonat@ufpr.br</a>

Author

Pseudo Akaike Information Criterion — pAIC

<dl>

<dt>object</dt>
<dd>an object or a list of objects representing a model
of <code>mcglm</code> class.</dd>


<dt>verbose</dt>
<dd>logical. Print or not the pAIC value.</dd>

</dl>

Wagner Hugo Bonat, <a href='mailto:wbonat@ufpr.br'>wbonat@ufpr.br</a>

pAIC: Pseudo Akaike Information Criterion

Description

Usage

Value

Arguments

Author

See Also