Run posterior sampling for a prepared one-arm bundle
run_mcmc_bundle_manual.Rdrun_mcmc_bundle_manual() is the explicit runner for objects created by
build_nimble_bundle. It compiles the stored NIMBLE code,
executes MCMC, and returns a "mixgpd_fit" object.
Usage
run_mcmc_bundle_manual(
bundle,
show_progress = TRUE,
quiet = FALSE,
parallel_chains = FALSE,
workers = NULL,
timing = FALSE,
z_update_every = NULL
)Arguments
- bundle
A
causalmixgpd_bundlefrombuild_nimble_bundle().- show_progress
Logical; if TRUE, print step messages and render progress where supported.
- quiet
Logical; if TRUE, suppress console status messages. Set to FALSE to see progress messages during MCMC setup and execution.
- parallel_chains
Logical; run chains concurrently when
nchains > 1.- workers
Optional integer number of workers for parallel execution.
- timing
Logical; if TRUE, include stage timings (
build,compile,mcmc) infit$timing.- z_update_every
Integer >= 1 controlling latent cluster-label update cadence.
Value
A fitted object of class "mixgpd_fit" containing posterior
draws, model metadata, and cached objects used by downstream S3 methods.
Details
The resulting fit supports posterior summaries of the model parameters as well as posterior predictive functionals such as \(f(y \mid x)\), \(S(y \mid x)\), \(Q(\tau \mid x)\), and restricted means.
If parallel_chains = TRUE, chains are run concurrently when the stored
MCMC configuration uses more than one chain. If the bundle was built with
latent cluster labels monitored, the z_update_every argument controls how
frequently those latent indicators are refreshed during sampling.
Examples
if (FALSE) { # \dontrun{
library(nimble)
y <- abs(rnorm(40)) + 0.1
bundle <- build_nimble_bundle(
y = y,
backend = "sb",
kernel = "normal",
GPD = FALSE,
components = 3,
mcmc = list(niter = 200, nburnin = 50, thin = 1, nchains = 1, seed = 1)
)
fit <- run_mcmc_bundle_manual(bundle, show_progress = FALSE)
fit
} # }