MCMCinput: Settings for the MCMC Algorithm

In geoCount: Analysis and Modeling for Geostatistical Count Data

Description

This function sets up the parameters and initial values used for the MCMC algorithms.

Usage

MCMCinput(run = 200, run.S = 1, rho.family = "rhoPowerExp", 
        Y.family = "Poisson", 
        priorSigma = "Halft", parSigma = c(1, 1),
        ifkappa = 0, 
        scales = c(0.5, 1.65^2 + 0.8, 0.8, 0.7, 0.15), 
        phi.bound = c(0.005, 1), 
        initials = list(c(1), 1.5, 0.2, 1))

Arguments

run	the number of iterations
run.S	the number of internal iterations for latent variables
rho.family	take the value of "rhoPowerExp", "rhoMatern", or "rhoSph" which indicates the powered exponential, Matern, or Spherical correlation function is used
Y.family	take the value of "Poisson" or "Binomial" which indicates Poisson or Binomial distribution for response variables
priorSigma	the prior distribution for σ, the options include "Halft" (positive-truncated t distribution), "InvGamma" (inverse gamma distribution), and "Reciprocal" (reciprocal distribution)
parSigma	the parameters for the prior distribution of σ: when priorSigma = "Halft" the first parameter is scale and the second is degree of freedom; when priorSigma = "InvGamma" the first parameter is shape and the second is scale; when priorSigma = "Reciprocal" both parameters are ignored
ifkappa	take zero or non-zero value which indicates whether κ should be sampled
scales	a vector which indicates the tuning parameters for (S, β, σ,φ,κ) respectively
phi.bound	the upper and lower bound for φ
initials	a list which indicates the initial values for (β, σ,φ,κ) respectively

Details

During each iteration of Gibbs sampling process, the group of latent variables is updated "run.S" times to improve accuracy and reduce autocorrelations.

Value

A list of setting parameters.

Author(s)

Liang Jing [e-mail]

See Also

runMCMC, runMCMC.sf.

Examples

## Not run: 
  input <- MCMCinput( run = 10000, run.S = 10, 
          rho.family = "rhoPowerExp", 
          Y.family = "Poisson", 
          priorSigma = "Halft", parSigma = c(1, 1),
          ifkappa=0,
          scales=c(0.5, 1.5, 0.9, 0.6, 0.5), 
          phi.bound=c(0.005, 1), 
          initials=list(c(-1, 2, 1), 1, 0.1, 1) )
  res <- runMCMC(Y, L=0, loc=loc, X=loc, MCMCinput = input )

## End(Not run)

geoCount documentation built on May 2, 2019, 12:38 a.m.