Spatio-Temporal Generalised Linear Mixed Models for Areal Unit Data
Spatio-Temporal Generalised Linear Mixed Models For Areal Unit Data
Extract the regression coefficients from a model.
Extract the fitted values from a model.
Extract the estimated loglikelihood from a fitted model.
Extract the model (design) matrix from a model.
Fit a multivariate spatio-temporal generalised linear mixed model to d...
Print a summary of the fitted model to the screen.
Extract the residuals from a model.
Fit a spatio-temporal generalised linear mixed model to data, with a s...
Fit a spatio-temporal generalised linear mixed model to data, with spa...
Fit a spatio-temporal generalised linear mixed model to data, with a s...
Fit a spatio-temporal generalised linear mixed model to data, with a c...
Fit a spatio-temporal generalised linear mixed model to data, where th...
Fit a spatio-temporal generalised linear mixed model to data, with a s...
Fit a spatio-temporal generalised linear mixed model to data, with a c...
Estimate an appropriate neighbourhood matrix for a set of spatial data...
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, including models similar to Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001>. Full details are given in the vignette accompanying this package. 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.
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