Data_LungCancer

<code>data.frame</code> object containing simulated lung cancer mortality data in the 7907 municipalities of continental Spain during the period 1991-2015.

data

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

Data_LungCancer function

Formal class <code>data.frame</code> with 197.675 rows and 5 colunmns.<ul>
<li>ID: character vector of geographic identifiers</li>
<li>year: numeric vector of year's identifiers</li>
<li>obs: observed number of cases</li>
<li>exp: expected number of cases</li>
<li>SMR: standardized mortality ratios</li>
<li>pop: population at risk</li>
</ul>

Data_LungCancer: Spanish lung cancer mortality data

Description

Usage

Arguments

Format