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.
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
)total number of simulations.
initial number of samples to be discarded.
value used to retain only evey thin-th sampled value.
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.
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.
starting value for the regression coefficients beta. If not provided the prior mean is used.
starting value for sigma2. If not provided the prior mean is used.
starting value for phi. If not provided the prior mean is used.
starting value for the spatial random effect. If not provided the prior mean is used.
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.
tuning parameter for the covariance matrix of the Gaussian proposal. Default is h=1.
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.
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.
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.
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.
an object of class "mcmc.Bayes.PrevMap".