make_hierarchical_mesh_basis

inla.spde2.pcmatern_B

Make hierarchical mesh basis functions

internal

Facilitates spatial and general latent Gaussian modeling using
integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>).
Additionally, extends the GAM-like model class to more general nonlinear predictor
expressions, and implements a log Gaussian Cox process likelihood for
modeling univariate and spatial point processes based on ecological survey data.
Model components are specified with general inputs and mapping methods to the
latent variables, and the predictors are specified via general R expressions,
with separate expressions for each observation likelihood model in
multi-likelihood models. A prediction method based on fast Monte Carlo sampling
allows posterior prediction of general expressions of the latent variables.
Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019)
<doi:10.1111/2041-210X.13168>.

Finn Lindgren

inlabru

Bayesian Latent Gaussian Modelling using INLA and Extensions

Fabian E. Bachl

David L. Borchers

Daniel Simpson

Lindesay Scott-Howard

Seaton Andy

Suen Man Ho

Roudier Pierre

Meehan Tim

Reddy Peddinenikalva Niharika

Perepolkin Dmytro

make_hierarchical_mesh_basis function

<ul>
<li><code>inla.spde2.pcmatern_B()</code>: Construct a pcmatern model with basis change
<a href="https://lifecycle.r-lib.org/articles/stages.html#experimental"><img src="figures/lifecycle-experimental.svg?package=inlabru&version=2.11.1" alt="[Experimental]"></a></li>
</ul>

Functions

Make hierarchical mesh basis functions — make_hierarchical_mesh_basis

<ul>
<li><code>inla.spde2.pcmatern_B()</code>: Construct a pcmatern model with basis change
<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a></li>
</ul>

Make hierarchical mesh basis functions

make_hierarchical_mesh_basis: Make hierarchical mesh basis functions

Description

Usage

Arguments

Functions