graphpcor-class

graphpcor.formula

graphpcor.matrix

print.graphpcor

summary.graphpcor

dim.graphpcor

edges,graphpcor-method

plot,graphpcor,ANY-method

Laplacian.graphpcor

chol,graphpcor-method

vcov,graphpcor-method

prec.graphpcor

Set a graph whose nodes and edges represent variables and
conditional distributions, respectively.

Implement some models for
correlation/covariance matrices including two approaches
to model correlation matrices from a graphical structure.
One use latent parent variables as proposed in
Sterrantino et. al. (2024) <doi:10.48550/arXiv.2312.06289>.
The other uses a graph to specify conditional
relations between the variables.
The graphical structure makes correlation matrices
interpretable and avoids the quadratic increase of
parameters as a function of the dimension.
In the first approach a natural sequence of simpler
models along with a complexity penalization is used.
The second penalizes deviations from a base model.
These can be used as prior for model parameters,
considering C code through the 'cgeneric' interface
for the 'INLA' package (<https://www.r-inla.org>).
This allows one to use these models as building
blocks combined and to other latent Gaussian models
in order to build complex data models.

Elias Teixeira Krainski

graphpcor

Models for Correlation Matrices Based on Graphs

Elias Krainski

Denis Rustand

Anna Freni-Sterrantino

Janet van Niekerk

Haavard Rue’

graphpcor-class function

<dl><dt>...</dt>
<dd>list of formula used to define the edges.</dd>
<dt>x</dt>
<dd>a <code>graphpcor</code> object</dd>
<dt>object</dt>
<dd>graphpcor object</dd>
<dt>which</dt>
<dd>not used</dd>
<dt>y</dt>
<dd>graphpcor</dd>
<dt>graph</dt>
<dd>graphpcor object, see <code>graphpcor</code>.</dd>
<dt>model</dt>
<dd>graphpcor model object</dd></dl>

Arguments

<ul>
<li><code>edges(graphpcor)</code>: Extract the edges of a <code>graphcor</code> to be used for plot</li>
<li><code>plot(x = graphpcor, y = ANY)</code>: The plot method for <code>graphpcor</code></li>
<li><code>chol(graphpcor)</code>: Build the unite diagonal lower triangle matrix</li>
<li><code>vcov(graphpcor)</code>: The <code>vcov</code> method for a <code>graphpcor</code></li>
</ul>

Methods (by generic)

<ul>
<li><code>graphpcor(formula)</code>: A <code>graphpcor</code> is a graph where a node represents
a variable and an edge a conditional distribution.</li>
<li><code>graphpcor(matrix)</code>: Build a <code>graphpcor</code> from a matrix</li>
<li><code>print(graphpcor)</code>: The print method for <code>graphpcor</code></li>
<li><code>summary(graphpcor)</code>: The summary method for <code>graphpcor</code></li>
<li><code>dim(graphpcor)</code>: The dim method for <code>graphpcor</code></li>
<li><code>Laplacian(graphpcor)</code>: The Laplacian method for a <code>graphpcor</code></li>
<li><code>prec(graphpcor)</code>: The precision method for 'graphpcor'</li>
</ul>

Functions

Set a graph whose nodes and edges represent variables and
conditional distributions, respectively. — graphpcor-class

<dl>

<dt>...</dt>
<dd>list of formula used to define the edges.</dd>


<dt>x</dt>
<dd>a <code>graphpcor</code> object</dd>


<dt>object</dt>
<dd>graphpcor object</dd>


<dt>which</dt>
<dd>not used</dd>


<dt>y</dt>
<dd>graphpcor</dd>


<dt>graph</dt>
<dd>graphpcor object, see <code>graphpcor</code>.</dd>


<dt>model</dt>
<dd>graphpcor model object</dd>

</dl>

Set a graph whose nodes and edges represent variables and
conditional distributions, respectively.

graphpcor-class: Set a graph whose nodes and edges represent variables and conditional distributions, respectively.

Description

Usage

Arguments

Methods (by generic)

Functions

Details

Examples