mcmc.samples | R Documentation |
Extract MCMC samples estimated by metropolis.krige()
mcmc.samples(object, as.matrix, as.data.frame, ...) ## S3 method for class 'krige' mcmc.samples(object, as.matrix = !as.data.frame, as.data.frame = FALSE, ...) ## S3 method for class 'summary.krige' mcmc.samples(object, as.matrix = !as.data.frame, as.data.frame = FALSE, ...) ## S3 method for class 'krige' as.matrix(x, ...) ## S3 method for class 'summary.krige' as.matrix(x, ...) ## S3 method for class 'krige' as.data.frame(x, ...) ## S3 method for class 'summary.krige' as.data.frame(x, ...)
object |
A |
as.matrix |
Logical values indicating if the output format should be a matrix. Defaults to |
as.data.frame |
Logical values indicating if the output format should be a
data.frame. Defaults to |
... |
Additional arguments passed to |
x |
A |
The function extracts the MCMC samples from the a krige
or summary.krige
object from the metropolis.krige
function. Users can define the output by using as.matrix
or as.data.frame
.
A summary.krige
list object.
as.mcmc.krige
## Not run: # Summarize Data summary(ContrivedData) # Initial OLS model contrived.ols<-lm(y~x.1+x.2,data=ContrivedData) # summary(contrived.ols) # Set seed set.seed(1241060320) 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) contrived.run.mat <- mcmc.samples(contrived.run) ### Alternatively, use generic methods contrived.run.mat <- as.matrix(contrived.run) contrived.run.df <- as.data.frame(contrived.run) ## End(Not run)
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