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mclcar (version 0.2-0)

Estimating Conditional Auto-Regressive (CAR) Models using Monte Carlo Likelihood Methods

Description

The likelihood of direct CAR models and Binomial and Poisson GLM with latent CAR variables are approximated by the Monte Carlo likelihood. The Maximum Monte Carlo likelihood estimator is found either by an iterative procedure of directly maximising the Monte Carlo approximation or by a response surface design method.Reference for the method can be found in the DPhil thesis in Z. Sha (2016). For application a good reference is R.Bivand et.al (2017) .

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Version

Install

install.packages('mclcar')

Monthly Downloads

21

Version

0.2-0

License

GPL (>= 2)

Maintainer

Zhe Sha

Last Published

January 8th, 2022

Functions in mclcar (0.2-0)

mclcar-package

mclcar
mcl.glm

Monte Carlo likelihood calculation for glm with latent CAR variables.
CAR.simLM

Simulate samples from a CAR model.
ScotCancer

Scottish lip cancer dataset from Clayton and Kaldor (1987)
mcl.dCAR

Monte Carlo likelihood calculation for direct CAR models.
OptimMCL.HCAR

Iterative procedure for maximising the Monte Carlo likelihood of the hierachical conditional auto-regressive models.
plot.rsmMCL

Plot the fitted response surfaces
OptimMCL

Iterative procedure for maximising the Monte Carlo likelihood
loglik.dCAR

Likelihood computing and parameter estimation for a direct CAR model.
mcl.HCAR

The Monte Carlo likelihood function of the HCAR model.
summary.rsmMCL

Summary the output from the response surface method of maximising the Monte Carlo likelihood
postZ

Sampling the CAR latent variables given the Binomial or Poisson observations in glm models.
sim.HCAR

Simulate samples from a HCAR model.
scotplot

Scottish lip cancer dataset from Clayton and Kaldor (1987) with geo information.
summary.OptimMCL.HCAR

Summary the output from the iterative procedure of maximising the Monte Carlo likelihood.
summary.OptimMCL

Summary the output from the iterative procedure of maximising the Monte Carlo likelihood.
rsmMCL

Response surface method for maximising the Monte Carlo likelihood