get.thinned.e0.mcmc {bayesLife} | R Documentation |
The function get.thinned.e0.mcmc
accesses
a thinned and burned version of the given MCMC set. create.thinned.e0.mcmc
creates such set.
get.thinned.e0.mcmc(mcmc.set, thin = 1, burnin = 0) create.thinned.e0.mcmc(mcmc.set, thin = 1, burnin = 0, output.dir = NULL, verbose = TRUE)
mcmc.set |
Object of class |
thin, burnin |
Thinning interval and burnin used for creating or identifying the thinned object. |
output.dir |
Directory for storing the thinned object. By default it is stored into the same directory as |
verbose |
Logical switching log messages on and off. |
The function create.thinned.e0.mcmc
is called from e0.predict
and thus, the resulting object contains exactly the same MCMCs used for generating projections.
The thinning is done as follows: The given burnin
is removed from the beginning of each chain in the original MCMC set. Then each chain is thinned by thin
using equal spacing and all chains are collapsed into one single chain per parameter. They are stored in output.dir
under the name ‘thinned_mcmc_t_b’ where t is the value of thin
and b the value of burnin
.
Both functions return an object of class bayesLife.mcmc.set
. get.thinned.e0.mcmc
returns NULL
if such object does not exist.
Hana Sevcikova
bayesLife.mcmc.set
, e0.predict
## Not run: sim.dir <- tempfile() m <- run.e0.mcmc(nr.chains = 2, iter = 60, thin = 2, output.dir = sim.dir, verbose = TRUE) pr <- e0.predict(m, burnin = 40, predict.jmale = FALSE) # creates thinned MCMCs mb <- get.thinned.e0.mcmc(m, thin = 2, burnin = 40) summary(mb, meta.only = TRUE) # length 20 = 2chains x (60-40)iters./2thin # the same chain as summary(pr$mcmc.set, meta.only = TRUE) unlink(sim.dir, recursive=TRUE) ## End(Not run)