bru_summarise

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

bru_summarise function

<dl><dt>data</dt>
<dd>A list of samples, each either numeric or a <code>data.frame</code></dd>
<dt>probs</dt>
<dd>A numeric vector of probabilities with values in <code>[0, 1]</code>,
passed to <code><a href="/link/stats%3A%3Aquantile?package=inlabru&version=2.11.1" data-mini-rdoc="inlabru::stats::quantile">stats::quantile</a></code></dd>
<dt>x</dt>
<dd>A <code>data.frame</code> of data columns that should be added to the summary data frame</dd>
<dt>cbind.only</dt>
<dd>If TRUE, only <code>cbind</code> the samples and return a matrix where each column is a sample</dd>
<dt>max_moment</dt>
<dd>integer, at least 2. Determines the largest moment
order information to include in the output. If <code>max_moment &gt; 2</code>,
includes "skew" (skewness, <code>E[(x-m)^3/s^3]</code>), and
if <code>max_moment &gt; 3</code>, includes
"ekurtosis" (excess kurtosis, <code>E[(x-m)^4/s^4] - 3</code>). Default 2.
Note that the Monte Carlo variability of the <code>ekurtois</code> estimate may be large.</dd></dl>

Arguments

Summarise and annotate data — bru_summarise

<dl>

<dt>data</dt>
<dd>A list of samples, each either numeric or a <code>data.frame</code></dd>


<dt>probs</dt>
<dd>A numeric vector of probabilities with values in <code>[0, 1]</code>,
passed to <code><a href='https://rdrr.io/r/stats/quantile.html'>stats::quantile</a></code></dd>


<dt>x</dt>
<dd>A <code>data.frame</code> of data columns that should be added to the summary data frame</dd>


<dt>cbind.only</dt>
<dd>If TRUE, only <code>cbind</code> the samples and return a matrix where each column is a sample</dd>


<dt>max_moment</dt>
<dd>integer, at least 2. Determines the largest moment
order information to include in the output. If <code>max_moment &gt; 2</code>,
includes "skew" (skewness, <code>E[(x-m)^3/s^3]</code>), and
if <code>max_moment &gt; 3</code>, includes
"ekurtosis" (excess kurtosis, <code>E[(x-m)^4/s^4] - 3</code>). Default 2.
Note that the Monte Carlo variability of the <code>ekurtois</code> estimate may be large.</dd>

</dl>

Summarise and annotate data

bru_summarise: Summarise and annotate data

Description

Usage

Value

Arguments

Examples