as.mcmc.krige | R Documentation |
krige
object to an mcmc
objectConvert MCMC matrix of posterior samples for use with the coda package
## S3 method for class 'krige' as.mcmc(x, start = 1, end = x$n.iter, thin = 1, ...) ## S3 method for class 'summary.krige' as.mcmc(x, start = 1, end = x$n.iter, thin = 1, ...)
x |
An |
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 |
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.
A mcmc
object.
coda::as.mcmc()
## 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 ## End(Not run)
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