posterior_crossvalidation

Leave-one-out crossvalidation for <code>graph_lme</code> models assuming observations at
the vertices of metric graphs

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

posterior_crossvalidation function

<dl><dt>object</dt>
<dd>A fitted model using the <code>graph_lme()</code> function or a named list of fitted objects using the <code>graph_lme()</code> function.</dd>
<dt>factor</dt>
<dd>Which factor to multiply the scores. The default is 1.</dd>
<dt>tibble</dt>
<dd>Return the scores as a <code><a href="https://tibble.tidyverse.org/reference/tibble.html">tidyr::tibble()</a></code></dd></dl>

Arguments

Leave-one-out crossvalidation for graph_lme models assuming observations at
the vertices of metric graphs — posterior_crossvalidation

<dl>

<dt>object</dt>
<dd>A fitted model using the <code>graph_lme()</code> function or a named list of fitted objects using the <code>graph_lme()</code> function.</dd>


<dt>factor</dt>
<dd>Which factor to multiply the scores. The default is 1.</dd>


<dt>tibble</dt>
<dd>Return the scores as a <code><a href='https://tibble.tidyverse.org/reference/tibble.html'>tidyr::tibble()</a></code></dd>

</dl>

posterior_crossvalidation: Leave-one-out crossvalidation for `graph_lme` models assuming observations at the vertices of metric graphs

Description

Usage

Value

Arguments