control.mcmc.Bayes.SPDE {PrevMap}

Control settings for the MCMC algorithm used for Bayesian inference using SPDE

Description

This function defines the different tuning parameter that are used in the MCMC algorithm for Bayesian inference using a SPDE approximation for the spatial Gaussian process.

Usage

control.mcmc.Bayes.SPDE(
  n.sim,
  burnin,
  thin,
  h.theta1 = 0.01,
  h.theta2 = 0.01,
  start.beta = "prior mean",
  start.sigma2 = "prior mean",
  start.phi = "prior mean",
  start.S = "prior mean",
  n.iter = 1,
  h = 1,
  c1.h.theta1 = 0.01,
  c2.h.theta1 = 1e-04,
  c1.h.theta2 = 0.01,
  c2.h.theta2 = 1e-04
)

Arguments

n.sim

total number of simulations.

burnin

initial number of samples to be discarded.

thin

value used to retain only evey thin-th sampled value.

h.theta1

starting value of the tuning parameter of the proposal distribution for \theta_{1} = \log(\sigma^2)/2. See 'Details' in binomial.logistic.Bayes or linear.model.Bayes.

h.theta2

starting value of the tuning parameter of the proposal distribution for \theta_{2} = \log(\sigma^2/\phi^{2 \kappa}). See 'Details' in binomial.logistic.Bayes or linear.model.Bayes.

start.beta

starting value for the regression coefficients beta. If not provided the prior mean is used.

start.sigma2

starting value for sigma2. If not provided the prior mean is used.

start.phi

starting value for phi. If not provided the prior mean is used.

start.S

starting value for the spatial random effect. If not provided the prior mean is used.

n.iter

number of iteration of the Newton-Raphson procedure used to compute the mean and coviariance matrix of the Gaussian proposal in the MCMC; defaut is n.iter=1.

h

tuning parameter for the covariance matrix of the Gaussian proposal. Default is h=1.

c1.h.theta1

value of c_{1} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter log(sigma2)/2; see 'Details' in binomial.logistic.Bayes or linear.model.Bayes.

c2.h.theta1

value of c_{2} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter log(sigma2)/2; see 'Details' in binomial.logistic.Bayes or linear.model.Bayes.

c1.h.theta2

value of c_{1} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter log(sigma2.curr/(phi.curr^(2*kappa))); see 'Details' in binomial.logistic.Bayes or linear.model.Bayes.

c2.h.theta2

value of c_{2} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter log(sigma2.curr/(phi.curr^(2*kappa))); see 'Details' in binomial.logistic.Bayes or linear.model.Bayes.

Value

an object of class "mcmc.Bayes.PrevMap".

Author(s)

Emanuele Giorgi [e-mail]

Peter J. Diggle [e-mail]