as.mcmc.bkmrfit {bkmrhat} | R Documentation |
Converts a kmrfit
(from the bkmr package) into
an mcmc
object from the coda
package. The
coda
package enables many different types of single chain MCMC
diagnostics, including geweke.diag
, traceplot
and
effectiveSize
. Posterior summarization is also available,
such as HPDinterval
and summary.mcmc
.
## S3 method for class 'bkmrfit' as.mcmc(x, iterstart = 1, thin = 1, ...)
x |
object of type kmrfit (from bkmr package) |
iterstart |
first iteration to use (e.g. for implementing burnin) |
thin |
keep 1/thin % of the total iterations (at regular intervals) |
... |
unused |
An mcmc
object
# following example from https://jenfb.github.io/bkmr/overview.html set.seed(111) library(coda) library(bkmr) dat <- bkmr::SimData(n = 50, M = 4) y <- dat$y Z <- dat$Z X <- dat$X set.seed(111) fitkm <- kmbayes(y = y, Z = Z, X = X, iter = 500, verbose = FALSE, varsel = FALSE) mcmcobj <- as.mcmc(fitkm, iterstart=251) summary(mcmcobj) # posterior summaries of model parameters # compare with default from bkmr package, which omits first 1/2 of chain summary(fitkm) # note this only works on multiple chains (see kmbayes_parallel) # gelman.diag(mcmcobj) # lots of functions in the coda package to use traceplot(mcmcobj) # will also fail with delta functions (when using variable selection) try(geweke.plot(mcmcobj))