Spatial Areal Unit Modelling


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Documentation for package ‘CARBayes’ version 4.0

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CARBayes-package Spatial Areal Unit Modelling
CARBayes Spatial Areal Unit Modelling
combine.data.shapefile Combine a data frame with a shapefile to create a SpatialPolygonsDataFrame object.
highlight.borders Create a SpatialPoints object identifying a subset of borders between neighbouring areas, which allows them to be overlaid on a spatial map.
print.carbayes Print a summary of the fitted model to the screen.
S.CARbym Fit a generalised linear model with a set of spatially autocorrelated random effect following the BYM conditional autoregressive (CAR) prior to spatial data.
S.CARcluster Fit a generalised linear model with a set of spatially autocorrelated random effects following a conditional autoregressive (CAR) prior and a piecewise constant jump component proposed by Lee and Sarran (2014) to spatial data.
S.CARdissimilarity Fit a generalised linear model with a set of spatially autocorrelated random effect following the localised conditional autoregressive (CAR) prior proposed by Lee and Mitchell (2012) to spatial data.
S.CARiar Fit a generalised linear model with a set of spatially autocorrelated random effect following the intrinsic conditional autoregressive (CAR) prior to spatial data.
S.CARleroux Fit a generalised linear model with a set of spatially autocorrelated random effect following the conditional autoregressive (CAR) prior proposed by Leroux et al. (1999) to spatial data.
S.independent Fit a generalised linear model with a set of independent random effect to spatial data.
spatialhousedata A SpatialPolygonsDataFrame object (from the sp package) containing property price data for the 271 Intermediate Geographies (IG) in the Greater Glasgow and Clyde health board.
spatialrespdata A SpatialPolygonsDataFrame object (from the sp package) containing respiratory hosptial admissions data for the 134 Intermediate Geographies (IG) to the north of the river Clyde in the Greater Glasgow and Clyde health board.
summarise.lincomb Compute the posterior distribution and quantiles of a linear combination of the covariate component of the linear predictor.
summarise.samples Summarise a matrix of Markov chain Monte Carlo samples.