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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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 |
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 |
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. 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 |
h |
tuning parameter for the covariance matrix of the Gaussian proposal. 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 |
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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