mat.mlogit

Find matrices <code>C</code> and <code>M</code> of e binary multivariate logistic 
 parameterization.

logistic model

Provides functions for defining
mixed graphs containing three types of edges, directed,
undirected and bi-directed, with possibly multiple edges.
These graphs are useful because they capture fundamental
independence structures in multivariate distributions
and in the induced distributions after marginalization
and conditioning.
The package is especially concerned with Gaussian graphical
models for
(i) ML estimation for directed acyclic graphs, undirected and
bi-directed graphs and ancestral graph models
(ii) testing several conditional independencies
(iii) checking global identification of DAG Gaussian models
with one latent variable
(iv) testing Markov equivalences and generating Markov
equivalent graphs of specific types.

Giovanni M Marchetti

Graphical Markov Models with Mixed Graphs

Giovanni M. Marchetti

Mathias Drton

Kayvan Sadeghi

mat.mlogit function

<dl> <dt>d</dt>
<dd>A positive integer, the number of binary responses.</dd> <dt>P</dt>
<dd>A list of vectors of integers specifying margins. For instance
<code>list(1, 2, c(1,2))</code>. Default: the power set of <code>1:d</code>.</dd></dl>

Arguments

Author

Multivariate logistic parametrization — mat.mlogit

<dl>

 <dt>d</dt>
<dd>A positive integer, the number of binary responses.</dd>

 <dt>P</dt>
<dd>A list of vectors of integers specifying margins. For instance
<code>list(1, 2, c(1,2))</code>. Default: the power set of <code>1:d</code>.</dd>

</dl>

mat.mlogit: Multivariate logistic parametrization

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples