Spatial Generalised Linear Mixed Models for Areal Unit Data

Documentation for package ‘CARBayes’ version 6.1.1

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CARBayes-package	Spatial Generalised Linear Mixed Models for Areal Unit Data
CARBayes	Spatial Generalised Linear Mixed Models for Areal Unit Data
fitted.CARBayes	Extract the fitted values from a model.
highlight.borders	Creates an sf data.frame object (from the sf package) identifying a subset of borders between neighbouring areas.
logLik.CARBayes	Extract the estimated loglikelihood from a fitted model.
model.matrix.CARBayes	Extract the model (design) matrix from a model.
MVS.CARleroux	Fit a multivariate spatial generalised linear mixed model to data, where the random effects are modelled by a multivariate conditional autoregressive model.
print.CARBayes	Print a summary of a fitted CARBayes model to the screen.
residuals.CARBayes	Extract the residuals from a model.
S.CARbym	Fit a spatial generalised linear mixed model to data, where the random effects have a BYM conditional autoregressive prior.
S.CARdissimilarity	Fit a spatial generalised linear mixed model to data, where the random effects have a localised conditional autoregressive prior.
S.CARleroux	Fit a spatial generalised linear mixed model to data, where the random effects have a Leroux conditional autoregressive prior.
S.CARlocalised	Fit a spatial generalised linear mixed model to data, where a set of spatially smooth random effects are augmented with a piecewise constant intercept process.
S.CARmultilevel	Fit a spatial generalised linear mixed model to multi-level areal unit data, where the spatial random effects have a Leroux conditional autoregressive prior.
S.glm	Fit a generalised linear model to data.
S.RAB	Fit a spatial generalised linear model with anisotropic basis functions to data for computationally efficient localised spatial smoothing, where the parameters are estimated by penalised maximum likelihood estimation with a ridge regression penalty.