control.mcmc.Bayes.SPDE: Control settings for the MCMC algorithm used for Bayesian...
In PrevMap: Geostatistical Modelling of Spatially Referenced Prevalence Data

Description Usage Arguments Value Author(s)

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
)

`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 θ_{1} = \log(σ^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 θ_{2} = \log(σ^2/φ^{2 κ}). 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`.