Description
Usage
Arguments
Value
Author(s)
View source: R/run_jags.R
Run parallel MCMC sampling using JAGS.
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12  (
,
jags_code,
pars,
ST,
cores,
,
n.chains,
n.iter,
n.adapt,
inits
)

data 
Data.frame or tibble in long format.

jags_code 
A string. JAGS model, usually returned by make_jagscode() .

pars 
Character vector of parameters to save/monitor.

ST 
A segment table (tibble), returned by get_segment_table .
Only really used when the model contains varying effects.

cores 
Positive integer or "all". Number of cores.

1 : serial sampling (Default). options(mc.cores = 3) will dominate cores = 1
but not larger values of cores .

>1 : parallel sampling on this number of cores. Ideally set chains
to the same value. Note: cores > 1 takes a few extra seconds the first
time it's called but subsequent calls will start sampling immediately.

"all" : use all cores but one and sets chains to the same value. This is
a convenient way to maximally use your computer's power.

sample 
One of

"post" (default): Sample the posterior.

"prior" : Sample only the prior. Plots, summaries, etc. will
use the prior. This is useful for prior predictive checks.

"both" : Sample both prior and posterior. Plots, summaries, etc.
will default to using the posterior. The prior only has effect when doing
SavageDickey density ratios in hypothesis .

"none" or FALSE : Do not sample. Returns an mcpfit
object without sample. This is useful if you only want to check
prior strings (fit$prior), the JAGS model (fit$jags_code), etc.

n.chains 
the number of parallel chains for the model

n.iter 
number of iterations to monitor

n.adapt 
the number of iterations for adaptation. See
adapt for details. If n.adapt = 0 then no
adaptation takes place.

inits 
A list if initial values for the parameters. This can be useful
if a model fails to converge. Read more in jags.model .
Defaults to NULL , i.e., no inits.

'mcmc.list“
Jonas Kristoffer Lindeløv jonas@lindeloev.dk
mcp documentation built on Aug. 3, 2020, 5:07 p.m.