coef_uncertainty

Compute confidence intervals for the fixed effects (upper 
and lower bound specifed by percentiles; default 90% confidence central interval)

A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.

Andrew ZammitMangion

Fixed Rank Kriging

Andrew Zammit-Mangion

Matthew Sainsbury-Dale

coef_uncertainty function

<dl><dt>object</dt>
<dd>object of class <code>SRE</code> returned from the constructor <code>SRE()</code> containing all the parameters and information on the SRE model</dd>
<dt>percentiles</dt>
<dd>(applicable only if <code>method</code> = "TMB") a vector of scalars in (0, 100) specifying the desired percentiles of the posterior predictive distribution; if <code>NULL</code>, no percentiles are computed</dd>
<dt>nsim</dt>
<dd>number of Monte Carlo samples used to compute the confidence intervals</dd>
<dt>random_effects</dt>
<dd>logical; if set to true, confidence intervals will also be provided for the random effects random effects &gamma; (see `?SRE` for details on these random effects)</dd></dl>

Arguments

Uncertainty quantification of the fixed effects — coef_uncertainty

<dl>

<dt>object</dt>
<dd>object of class <code>SRE</code> returned from the constructor <code>SRE()</code> containing all the parameters and information on the SRE model</dd>


<dt>percentiles</dt>
<dd>(applicable only if <code>method</code> = "TMB") a vector of scalars in (0, 100) specifying the desired percentiles of the posterior predictive distribution; if <code>NULL</code>, no percentiles are computed</dd>


<dt>nsim</dt>
<dd>number of Monte Carlo samples used to compute the confidence intervals</dd>


<dt>random_effects</dt>
<dd>logical; if set to true, confidence intervals will also be provided for the random effects random effects &gamma; (see `?SRE` for details on these random effects)</dd>

</dl>

coef_uncertainty: Uncertainty quantification of the fixed effects

Description

Usage

Arguments