evaluate_predictor

A list where each element is a list of named latent state
information, as produced by <code><a rd-options="=evaluate_state" href="/link/evaluate_state()?package=inlabru&version=2.3.1&to=%3Devaluate_state" data-mini-rdoc="=evaluate_state::evaluate_state()">evaluate_state()</a></code>

state

A <code>list</code>, <code>data.frame</code>, or <code>Spatial*DataFrame</code>, with coordinates
and covariates needed to evaluate the model.

data

A list where each element is list of named evaluated effects,
as computed by <code><a rd-options="=evaluate_effect_multi.component_list" href="/link/evaluate_effect_multi.component_list()?package=inlabru&version=2.3.1&to=%3Devaluate_effect_multi.component_list" data-mini-rdoc="=evaluate_effect_multi.component_list::evaluate_effect_multi.component_list()">evaluate_effect_multi.component_list()</a></code>

effects

predictor

character; determines the storage format of the output.
Available options:<ul>
<li><code>"auto"</code> If the first evaluated result is a vector or single-column matrix,
the "matrix" format is used, otherwise "list".</li>
<li><code>"matrix"</code> A matrix where each column contains the evaluated predictor
expression for a state.</li>
<li><code>"list"</code> A list where each column contains the evaluated predictor
expression for a state.</li>
</ul>Default: "auto"

format

Evaluate component effects or expressions, based on a bru model and one or
several states of the latent variables and hyperparameters.

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

evaluate_predictor function

A list where each element is a list of named latent state
information, as produced by <code><a rd-options='=evaluate_state' href='evaluate_state()'>evaluate_state()</a></code>

A list where each element is list of named evaluated effects,
as computed by <code><a rd-options='=evaluate_effect_multi.component_list' href='evaluate_effect_multi.component_list()'>evaluate_effect_multi.component_list()</a></code>

character; determines the storage format of the output.
Available options:<ul>
<li><code>"auto"</code> If the first evaluated result is a vector or single-column matrix,
the "matrix" format is used, otherwise "list".</li>
<li><code>"matrix"</code> A matrix where each column contains the evaluated predictor
expression for a state.</li>
<li><code>"list"</code> A list where each column contains the evaluated predictor
expression for a state.</li>
</ul>

Default: "auto"

evaluate_predictor: Evaluate component effects or expressions

Description

Usage

Arguments

Value