INLAtools-methods

initial

prior

graph

prec

prec.default

vcov,Matrix-method

For a given model object query the <code>initial</code>,
<code>mu</code>, log <code>prior</code>, <code>graph</code> or precision <code>prec</code>
can be evaluated/retrieved.

Contain code to work with a C struct, in short
cgeneric, to define a Gaussian Markov random (GMRF) model.
The cgeneric contain code to specify GMRF elements such as
the graph and the precision matrix, and also the initial and
prior for its parameters, useful for model inference.
It can be accessed from a C program and is the recommended
way to implement new GMRF models in the 'INLA' package
(<https://www.r-inla.org>).
The 'INLAtools' implement functions to evaluate
each one of the model specifications from R.
The implemented functionalities leverage the use
of 'cgeneric' models and provide a way to debug
the code as well to work with the prior for the
model parameters and to sample from it.
A very useful functionality is the Kronecker product method
that creates a new model from multiple cgeneric models.
It also works with the rgeneric, the R version of the
cgeneric intended to easy try implementation of new GMRF models.
The Kronecker between two cgeneric models was used in
Sterrantino et. al. (2024) <doi:10.1007/s10260-025-00788-y>,
and can be used to build the spatio-temporal intrinsic interaction
models for what the needed constraints are automatically set.

Elias Teixeira Krainski

INLAtools

Functionalities for the 'INLA' Package

Finn Lindgren

Haavard Rue’

INLAtools-methods function

<dl><dt>model</dt>
<dd>object to represent a model</dd>
<dt>theta</dt>
<dd>numeric vector.
For <code>prior</code> it can be a numeric matrix,
with number of lines equal the size of <code>theta</code>
and each column as a different case.</dd>
<dt>optimize</dt>
<dd>logical indicating if it is to be
returned only the elements and not as a sparse matrix.</dd>
<dt>...</dt>
<dd>additional arguments passed on</dd>
<dt>object</dt>
<dd>Matrix supposed to be a
sparse precision matrix</dd></dl>

Arguments

<ul>
<li><code>initial()</code>: Retrieve the initial model parameter(s)</li>
<li><code>mu()</code>: Evaluate the model's mean</li>
<li><code>prior()</code>: Evaluate the log-prior for a given <code>theta</code></li>
<li><code>graph()</code>: Retrieve the models' graph</li>
<li><code>prec()</code>: Retrieve the precision for a given <code>theta</code></li>
<li><code>prec(default)</code>: The default precision method
computes the inverse of the variance</li>
<li><code>vcov(Matrix)</code>: The <code>vcov</code> method for sparse matrices</li>
</ul>

Functions

Methods to work with a model. — INLAtools-methods

<dl>

<dt>model</dt>
<dd>object to represent a model</dd>


<dt>theta</dt>
<dd>numeric vector.
For <code>prior</code> it can be a numeric matrix,
with number of lines equal the size of <code>theta</code>
and each column as a different case.</dd>


<dt>optimize</dt>
<dd>logical indicating if it is to be
returned only the elements and not as a sparse matrix.</dd>


<dt>...</dt>
<dd>additional arguments passed on</dd>


<dt>object</dt>
<dd>Matrix supposed to be a
sparse precision matrix</dd>

</dl>

INLAtools-methods: Methods to work with a `model`.

Description

Usage

Value

Arguments

Functions

See Also