Run multiple BKMR chains in parallel
Fit parallel chains from the kmbayes function.
These chains leverage parallel processing from the future package, which
can speed fitting and enable diagnostics that rely on multiple Markov
chains from dispersed initial values.
kmbayes_parallel(nchains = 4, ...)
nchains |
number of parallel chains |
... |
arguments to kmbayes |
a "bkmrfit.list" object, which is just an R list object in which each entry is a "bkmrfit" object kmbayes
set.seed(111) dat <- bkmr::SimData(n = 50, M = 4) y <- dat$y Z <- dat$Z X <- dat$X set.seed(111) Sys.setenv(R_FUTURE_SUPPORTSMULTICORE_UNSTABLE="quiet") future::plan(strategy = future::multiprocess, workers=2) # only 50 iterations fit to save installation time fitkm.list <- kmbayes_parallel(nchains=2, y = y, Z = Z, X = X, iter = 50, verbose = FALSE, varsel = TRUE) closeAllConnections()
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