summary.graph_lme

Function providing a summary of results related to metric graph
mixed effects regression 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

summary.graph_lme function

<dl><dt>object</dt>
<dd>an object of class <code>graph_lme</code> containing results from the
fitted model.</dd>
<dt>all_times</dt>
<dd>Show all computed times.</dd>
<dt>...</dt>
<dd>not used.</dd></dl>

Arguments

Summary Method for graph_lme Objects — summary.graph_lme

<dl>

<dt>object</dt>
<dd>an object of class <code>graph_lme</code> containing results from the
fitted model.</dd>


<dt>all_times</dt>
<dd>Show all computed times.</dd>


<dt>...</dt>
<dd>not used.</dd>

</dl>

summary.graph_lme: Summary Method for `graph_lme` Objects

Description

Usage

Value

Arguments