krige (version 0.6.2)

as.mcmc.krige: Convert krige object to an mcmc object

Description

Convert MCMC matrix of posterior samples for use with the coda package

Usage

# S3 method for krige
as.mcmc(x, start = 1, end = x$n.iter, thin = 1, ...)
# S3 method for summary.krige
as.mcmc(x, start = 1, end = x$n.iter, thin = 1, ...)

Arguments

x

An krige or summary.krige object.

start

The iteration number of the first observation.

end

The iteration number of the last observation.

thin

The thinning interval between consecutive observations.

…

Additional arguments to be passed to mcmc() methods of coda package.

Value

A mcmc object.

Details

The function converts a krige output object to a Markov Chain Monte Carlo (mcmc) object used in coda as well as a variety of MCMC packages. It extracts the MCMC matrix of posterior samples from the output of metropolis.krige for further use with other MCMC packages and functions.

See Also

coda::as.mcmc()

Examples

# NOT RUN {
# Summarize Data
summary(ContrivedData)

# Set seed
set.seed(1241060320)

#For simple illustration, we set to few iterations.
#In this case, a 10,000-iteration run converges to the true parameters.
#If you have considerable time and hardware, delete the # on the next line.
#10,000 iterations took 39 min. with 8 GB RAM & a 1.5 GHz Quad-Core processor.
M <- 100
#M<-10000

contrived.run <- metropolis.krige(y ~ x.1 + x.2, coords = c("s.1","s.2"), 
                                  data = ContrivedData, n.iter = M, n.burnin = 20,
                                  range.tol = 0.05)
                                  
# Convert to mcmc object
mcmc.contrived.run <- as.mcmc(contrived.run)
#mcmc.contrived.run <- as.mcmc(summary(contrived.run))

# Diagnostics using MCMC packages
coda::raftery.diag(mcmc.contrived.run)
# superdiag::superdiag(mcmc.contrived.run) #NOT WORKING YET
# }
# NOT RUN {
# }