Description Usage Arguments Value Author(s)
This function defines the different tuning parameter that are used in the MCMC algorithm for Bayesian inference.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | 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 |
h.theta1 |
starting value of the tuning parameter of the proposal distribution for θ_{1} = \log(σ^2)/2. See 'Details' in |
h.theta2 |
starting value of the tuning parameter of the proposal distribution for θ_{2} = \log(σ^2/φ^{2 κ}). See 'Details' in |
h.theta3 |
starting value of the tuning parameter of the proposal distribution for θ_{3} = \log(τ^2). See 'Details' in |
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 |
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 |
start.beta |
starting value for the regression coefficients |
start.sigma2 |
starting value for |
start.phi |
starting value for |
start.S |
starting value for the spatial random effect. |
start.nugget |
starting value for the variance of the nugget effect; default is |
c1.h.theta1 |
value of c_{1} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter |
c2.h.theta1 |
value of c_{2} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter |
c1.h.theta2 |
value of c_{1} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter |
c2.h.theta2 |
value of c_{2} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter |
c1.h.theta3 |
value of c_{1} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter |
c2.h.theta3 |
value of c_{2} used to adaptively tune the variance of the Gaussian proposal for the transformed parameter |
linear.model |
logical; if |
binary |
logical; if |
an object of class "mcmc.Bayes.PrevMap".
Emanuele Giorgi e.giorgi@lancaster.ac.uk
Peter J. Diggle p.diggle@lancaster.ac.uk
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