control.mcmc.Bayes: 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.

control.mcmc.Bayes(
  n.sim,
  burnin,
  thin,
  h.theta1 = 0.01,
  h.theta2 = 0.01,
  h.theta3 = 0.01,
  L.S.lim = NULL,
  epsilon.S.lim = NULL,
  start.beta = "prior mean",
  start.sigma2 = "prior mean",
  start.phi = "prior mean",
  start.S = "prior mean",
  start.nugget = "prior mean",
  c1.h.theta1 = 0.01,
  c2.h.theta1 = 1e-04,
  c1.h.theta2 = 0.01,
  c2.h.theta2 = 1e-04,
  c1.h.theta3 = 0.01,
  c2.h.theta3 = 1e-04,
  linear.model = FALSE,
  binary = FALSE
)

`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`.
`h.theta3`	starting value of the tuning parameter of the proposal distribution for θ_{3} = \log(τ^2). See 'Details' in `binomial.logistic.Bayes` or `linear.model.Bayes`.
`L.S.lim`	an atomic value or a vector of length 2 that is used to define the number of steps used at each iteration in the Hamiltonian Monte Carlo algorithm to update the spatial random effect; if a single value is provided than the number of steps is kept fixed, otherwise if a vector of length 2 is provided the number of steps is simulated at each iteration as `floor(runif(1,L.S.lim[1],L.S.lim[2]+1))`.
`epsilon.S.lim`	an atomic value or a vector of length 2 that is used to define the stepsize used at each iteration in the Hamiltonian Monte Carlo algorithm to update the spatial random effect; if a single value is provided than the stepsize is kept fixed, otherwise if a vector of length 2 is provided the stepsize is simulated at each iteration as `runif(1,epsilon.S.lim[1],epsilon.S.lim[2])`.
`start.beta`	starting value for the regression coefficients `beta`.
`start.sigma2`	starting value for `sigma2`.
`start.phi`	starting value for `phi`.
`start.S`	starting value for the spatial random effect.
`start.nugget`	starting value for the variance of the nugget effect; default is `NULL` if the nugget effect is not present.
`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`.
`c1.h.theta3`	value of c_{1} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter `log(tau2)`; see 'Details' in `binomial.logistic.Bayes` or `linear.model.Bayes`.
`c2.h.theta3`	value of c_{2} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter `log(tau2)`; see 'Details' in `binomial.logistic.Bayes` or `linear.model.Bayes`.
`linear.model`	logical; if `linear.model=TRUE`, the control parameters are set for the geostatistical linear model. Default is `linear.model=FALSE`.
`binary`	logical; if `binary=TRUE`, the control parameters are set the binary geostatistical model. Default is `binary=FALSE`.