CARBayesST (version 2.1)

CARBayesST-package: Spatio-Temporal Generalised Linear Mixed Models For Areal Unit Data

Description

Implements a class of 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. The spatio-temporal autocorrelation is modelled by random effects, which are assigned conditional autoregressive (CAR) 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 of this package was supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1 and the Medical Research Council (MRC) grant MR/L022184/1.

Details

ll{ Package: CARBayesST Type: Package Version: 2.1 Date: 2015-09-04 License: GPL (>= 2) }

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 Sarran, C (2015). Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies, Environmetrics, to appear. Rushworth, A., Lee, D., and Sarran, C. An adaptive spatio-temporal smoothing model for estimating trends and step changes in disease risk (2014a) arXiv:1411.0924

Rushworth, A., D. Lee, and R. Mitchell (2014b). 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.

Examples

## See the examples in the function specific help files and in the vignette
## accompanying this package.