spatialgibbs: Fit Hierarchical Model with Spatial Covariance

In rdpeng/tsmodel: Time Series Modeling for Air Pollution and Health

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 = 1e+05
)

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.

rdpeng/tsmodel documentation built on May 18, 2022, 5:52 a.m.