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CARBayes (version 4.5)

Spatial Generalised Linear Mixed Models for Areal Unit Data

Description

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. The response variable can be binomial, Gaussian or Poisson. Spatial autocorrelation is modelled by a set of random effects, which are assigned a conditional autoregressive (CAR) prior distribution. A number of different CAR priors are available for the random effects, including a multivariate CAR (MCAR) model for multivariate spatial data. 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 was supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1.

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Version

Install

install.packages('CARBayes')

Monthly Downloads

847

Version

4.5

License

GPL (>= 2)

Maintainer

Duncan Lee

Last Published

May 6th, 2016

Functions in CARBayes (4.5)

highlight.borders

Creates a SpatialPoints object identifying a subset of borders between neighbouring areas.
combine.data.shapefile

Combines a data frame with a shapefile to create a SpatialPolygonsDataFrame object.
summarise.samples

Summarise a matrix of Markov chain Monte Carlo samples.
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.
summarise.lincomb

Compute the posterior distribution for a linear combination of the covariates from the linear predictor.
S.CARleroux

Fit a spatial generalised linear mixed model to data, where the random effects have a Leroux conditional autoregressive prior.
S.CARdissimilarity

Fit a spatial generalised linear mixed model to data, where the random effects have a localised conditional autoregressive prior.
print.carbayes

Print a summary of a fitted carbayes model to the screen.
CARBayes-package

Spatial Generalised Linear Mixed Models for Areal Unit Data
S.CARbym

Fit a spatial generalised linear mixed model to data, where the random effects have a BYM conditional autoregressive prior.
MVS.CARleroux

Fit a multivariate spatial generalised linear mixed model to data, where the random effects are modelled by a multivariate conditional autoregressive model.