Spatial Generalised Linear Mixed Models for Areal Unit Data
Spatial Generalised Linear Mixed Models for Areal Unit Data
Extract the fitted values from a model.
Creates an sf data.frame object (from the sf package) identifying a su...
Extract the estimated loglikelihood from a fitted model.
Extract the model (design) matrix from a model.
Fit a multivariate spatial generalised linear mixed model to data, whe...
Print a summary of a fitted CARBayes model to the screen.
Extract the residuals from a model.
Fit a spatial generalised linear mixed model to data, where the random...
Fit a spatial generalised linear mixed model to data, where the random...
Fit a spatial generalised linear mixed model to data, where the random...
Fit a spatial generalised linear mixed model to data, where a set of s...
Fit a spatial generalised linear mixed model to multi-level areal unit...
Fit a generalised linear model to data.
Fit a spatial generalised linear model with anisotropic basis function...
Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, <doi:10.1007/BF00116466>) and Leroux model (Leroux et al., 2000, <doi:10.1007/978-1-4612-1284-3_4>). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.