MCMCinput {geoCount} | R Documentation |
This function sets up the parameters and initial values used for the MCMC algorithms.
MCMCinput(run = 200, run.S = 1, rho.family = "rhoPowerExp", Y.family = "Poisson", priorSigma = "Halft", parSigma = c(1, 1), ifkappa = 0, scales = c(0.5, 1.65^2 + 0.8, 0.8, 0.7, 0.15), phi.bound = c(0.005, 1), initials = list(c(1), 1.5, 0.2, 1))
run |
the number of iterations |
run.S |
the number of internal iterations for latent variables |
rho.family |
take the value of |
Y.family |
take the value of |
priorSigma |
the prior distribution for σ, the options include "Halft" (positive-truncated t distribution), "InvGamma" (inverse gamma distribution), and "Reciprocal" (reciprocal distribution) |
parSigma |
the parameters for the prior distribution of σ: when priorSigma = "Halft" the first parameter is scale and the second is degree of freedom; when priorSigma = "InvGamma" the first parameter is shape and the second is scale; when priorSigma = "Reciprocal" both parameters are ignored |
ifkappa |
take zero or non-zero value which indicates whether κ should be sampled |
scales |
a vector which indicates the tuning parameters for (S, β, σ,φ,κ) respectively |
phi.bound |
the upper and lower bound for φ |
initials |
a list which indicates the initial values for (β, σ,φ,κ) respectively |
During each iteration of Gibbs sampling process, the group of latent variables is updated "run.S" times to improve accuracy and reduce autocorrelations.
A list of setting parameters.
Liang Jing ljing918@gmail.com
## Not run: input <- MCMCinput( run = 10000, run.S = 10, rho.family = "rhoPowerExp", Y.family = "Poisson", priorSigma = "Halft", parSigma = c(1, 1), ifkappa=0, scales=c(0.5, 1.5, 0.9, 0.6, 0.5), phi.bound=c(0.005, 1), initials=list(c(-1, 2, 1), 1, 0.1, 1) ) res <- runMCMC(Y, L=0, loc=loc, X=loc, MCMCinput = input ) ## End(Not run)