run_jags: Run parallel MCMC sampling using JAGS.
In mcp: Regression with Multiple Change Points

run_jags

R Documentation

Run parallel MCMC sampling using JAGS.

Description

Run parallel MCMC sampling using JAGS.

Usage

run_jags(
  data,
  jags_code,
  pars,
  ST,
  cores,
  sample,
  n.chains,
  n.iter,
  n.adapt,
  inits
)

Arguments

`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. `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"`: 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 Savage-Dickey 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.