ref.summary {ref.ICAR}

Parameter Summaries for MCMC Analysis

Description

Takes a matrix of MCMC chains, iterations, and acceptance values to return posterior summaries of the parameters, including posterior medians, intervals, and acceptance rates.

Usage

ref.summary(
  MCMCchain,
  tauc.MCMC,
  sigma2.MCMC,
  beta.MCMC,
  phi.MCMC,
  accept.phi,
  accept.sigma2,
  accept.tauc,
  iters = 10000,
  burnin = 5000
)

Arguments

MCMCchain

Matrix of MCMC chains for the ICAR model parameters.

tauc.MCMC

MCMC chains for the spatial dependence parameter.

sigma2.MCMC

MCMC chains for the variance of the unstructured random effects.

beta.MCMC

MCMC chains for the fixed effect regression coefficients.

phi.MCMC

MCMC chains for the spatial random effects.

accept.phi

Final acceptance number for spatial random effects.

accept.sigma2

Final acceptance number for variance of the unstructured random effects.

accept.tauc

Final acceptance number for the spatial dependence parameter.

iters

Number of MCMC iterations in MCMCchain.

burnin

Number of MCMC iterations discarded as burn-in for MCMCchain.

Value

Parameter summaries

beta.median

Posterior medians of the fixed effect regression coefficients.

beta.hpd

Highest Posterior Density intervals for the fixed effect regression coefficients.

tauc.median

Posterior median of the spatial dependence parameter.

tauc.hpd

Highest Posterior Density interval for the spatial dependence parameter.

sigma2.median

Posterior median of the unstructured random effects variance.

sigma2.hpd

Highest Posterior Density interval for the unstructured random effects variance.

tauc.accept

Final acceptance rate for the spatial dependence parameter.

sigma2.accept

Final acceptance rate for the unstructured random effects variance.

Author(s)

Erica M. Porter, Matthew J. Keefe, Christopher T. Franck, and Marco A.R. Ferreira

Examples

## Refer to the vignette attached to the package.