spatialgibbs {tsModel}

Fit Hierarchical Model with Spatial Covariance

Description

This function fits a Normal hierarchical model with a spatial covariance structure via MCMC.

Usage

spatialgibbs(b, v, x, y, phi = 0.1, scale = 1, maxiter = 1000,
             burn = 500, a0 = 10, b0 = 100000)

Arguments

b

a vector of regression coefficients

v

a vector of regression coefficient variances

x

a vector of x-coordinates

y

a vector of y-coordinates

phi

scale parameter for exponential covariance function

scale

scaling parameter for the prior variance of the national average estimate

maxiter

maximum number of iterations in the Gibbs sampler

burn

number of iterations to discard

a0

parameter for Gamma prior on heterogeneity variance

b0

parameter for Gamma prior on heterogeneity variance

Details

This function is used to produce pooled national average estimates of air pollution risks taking into account potential spatial correlation between the risks. The function uses a Markov chain Monte Carlo sampler to produce the posterior distribution of the national average estimate and the heterogeneity variance. See the reference below for more details.

Author(s)

Roger D. Peng [e-mail]

References

Peng RD, Dominic F (2008). Statistical Methods for Environmental Epidemiology in R: A Case Study in Air Pollution and Health, Springer.