| CARBayesST-package {CARBayesST} | R Documentation |
Spatio-Temporal Generalised Linear Mixed Models For Areal Unit Data
Description
Implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, and full details are given in the vignette accompanying this package and the references below. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1.
Details
| Package: | CARBayesST |
| Type: | Package |
| Version: | 4.0 |
| Date: | 2023-10-31 |
| License: | GPL (>= 2) |
Author(s)
Author: Duncan Lee, Alastair Rushworth, Gary Napier and William Pettersson
Maintainer: Duncan Lee <Duncan.Lee@glasgow.ac.uk>
References
Bernardinelli, L., D. Clayton, C.Pascuto, C.Montomoli, M.Ghislandi, and M. Songini (1995). Bayesian analysis of space-time variation in disease risk. Statistics in Medicine, 14, 2433-2443.
Knorr-Held, L. (2000). Bayesian modelling of inseparable space-time variation in disease risk. Statistics in Medicine, 19, 2555-2567.
Lee, D and Lawson, C (2016). Quantifying the spatial inequality and temporal trends in maternal smoking rates in Glasgow, Annals of Applied Statistics, 10, 1427-1446.
Lee, D and Rushworth, A and Napier, G (2018). Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package, Journal of Statistical Software, 84, 9, 1-39.
Lee, D and Meeks, K and Pettersson, W (2021). Improved inference for areal unit count data using graph-based optimisation. Statistics and Computing, 31:51.
Lee D, Robertson C, and Marques, D (2022). Quantifying the small-area spatio-temporal dynamics of the Covid-19 pandemic in Scotland during a period with limited testing capacity, Spatial Statistics, https://doi.org/10.1016/j.spasta.2021.100508.
Napier, G, D. Lee, C. Robertson, A. Lawson, and K. Pollock (2016). A model to estimate the impact of changes in MMR vaccination uptake on inequalities in measles susceptibility in Scotland, Statistical Methods in Medical Research, 25, 1185-1200.
Napier, G., Lee, D., Robertson, C., and Lawson, A. (2019). A Bayesian space-time model for clustering areal units based on their disease trends, Biostatistics, 20, 681-697.
Rushworth, A., D. Lee, and R. Mitchell (2014). A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London. Spatial and Spatio-temporal Epidemiology 10, 29-38.
Rushworth, A., Lee, D., and Sarran, C (2017). An adaptive spatio-temporal smoothing model for estimating trends and step changes in disease risk. Journal of the Royal Statistical Society Series C, 66, 141-157.
Examples
## See the examples in the function specific help files and in the vignette
## accompanying this package.