graph_spde_basis

Constructs observation/prediction weight matrices
for metric graph models.

Facilitates creation and manipulation of metric graphs, such as street or river networks. Further facilitates operations and visualizations of data on metric graphs, and the creation of a large class of random fields and stochastic partial differential equations on such spaces. These random fields can be used for simulation, prediction and inference. In particular, linear mixed effects models including random field components can be fitted to data based on computationally efficient sparse matrix representations. Interfaces to the R packages 'INLA' and 'inlabru' are also provided, which facilitate working with Bayesian statistical models on metric graphs. The main references for the methods are Bolin, Simas and Wallin (2024) <doi:10.3150/23-BEJ1647>, Bolin, Kovacs, Kumar and Simas (2023) <doi:10.1090/mcom/3929> and Bolin, Simas and Wallin (2023) <doi:10.48550/arXiv.2304.03190> and <doi:10.48550/arXiv.2304.10372>.

David Bolin

MetricGraph

Random Fields on Metric Graphs

Alexandre Simas

Jonas Wallin

graph_spde_basis function

<dl><dt>graph_spde</dt>
<dd>An <code>inla_metric_graph_spde</code> object built with the
<code>graph_spde()</code> function.</dd>
<dt>repl</dt>
<dd>Which replicates? If there is no replicates, or to
use all replicates, one can set to <code>NULL</code>.</dd>
<dt>drop_na</dt>
<dd>Should the rows with at least one NA for one of the columns be removed? DEFAULT is <code>FALSE</code>.</dd>
<dt>drop_all_na</dt>
<dd>Should the rows with all variables being NA be removed? DEFAULT is <code>TRUE</code>.</dd></dl>

Arguments

Deprecated - Observation/prediction matrices for 'SPDE' models — graph_spde_basis

<dl>

<dt>graph_spde</dt>
<dd>An <code>inla_metric_graph_spde</code> object built with the
<code>graph_spde()</code> function.</dd>


<dt>repl</dt>
<dd>Which replicates? If there is no replicates, or to
use all replicates, one can set to <code>NULL</code>.</dd>


<dt>drop_na</dt>
<dd>Should the rows with at least one NA for one of the columns be removed? DEFAULT is <code>FALSE</code>.</dd>


<dt>drop_all_na</dt>
<dd>Should the rows with all variables being NA be removed? DEFAULT is <code>TRUE</code>.</dd>

</dl>

graph_spde_basis: Deprecated - Observation/prediction matrices for 'SPDE' models

Description

Usage

Value

Arguments