Mmodel_compute_cor

This function takes a <code>inla</code> object fitted using the <code>MCAR_INLA</code> function and computes the correlation coefficients between diseases.

Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2023 <doi:10.1016/j.cmpb.2023.107403> and Vicente et al., 2023 <doi:10.1007/s11222-023-10263-x>). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).

Aritz Adin

bigDM

Scalable Bayesian Disease Mapping Models for High-Dimensional
Data

Erick Orozco-Acosta

Maria Dolores Ugarte

Mmodel_compute_cor function

<dl><dt>model</dt>
<dd>object of class <code>inla</code> fitted using the <code>MCAR_INLA</code> function.</dd>
<dt>n.sample</dt>
<dd>numeric; number of samples to generate from the approximated joint posterior for the hyperparameters (see <code>help(inla.hyperpar.sample)</code>). Default to 1000.</dd></dl>

Arguments

Compute correlation coefficients between diseases — Mmodel_compute_cor

<dl>

<dt>model</dt>
<dd>object of class <code>inla</code> fitted using the <code>MCAR_INLA</code> function.</dd>


<dt>n.sample</dt>
<dd>numeric; number of samples to generate from the approximated joint posterior for the hyperparameters (see <code>help(inla.hyperpar.sample)</code>). Default to 1000.</dd>

</dl>

Mmodel_compute_cor: Compute correlation coefficients between diseases

Description

Usage

Value

Arguments