Description Usage Arguments Value See Also Examples

Allows rapid calculation of summaries and diagnostics from **specific nodes**
stored in `mcmc.list`

objects.

1 2 3 4 5 6 7 8 9 10 11 |

`post` |
A |

`params` |
A vector of regular expressions specifying the nodes to match for summarization.
Accepts multi-element vectors to match more than one node at a time.
See |

`digits` |
Control rounding of summaries.
Passed to |

`probs` |
Posterior quantiles to calculate. Passed to |

`Rhat` |
Calculate the Rhat convergence diagnostic using |

`neff` |
Calculate the number of effective MCMC samples using |

`mcse` |
Calculate the Monte Carlo standard error for the posterior mean and reported quantiles
using the |

`by_chain` |
Calculate posterior summaries for each chain
rather than for the aggregate across chains? Defaults to |

`auto_escape` |
Automatically escape |

A `matrix`

object with summary statistics as rows and nodes as columns.
If `by_chain = TRUE`

, an `array`

with chain-specific summaries as the third dimension is returned instead.

`match_params()`

, `coda::gelman.diag()`

, `coda::effectiveSize()`

, `mcmcse::mcse()`

, `mcmcse::mcse.q()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
# load example mcmc.list
data(cjs)
# calculate posterior summaries for the "p" nodes
# ("p[1]" doesn't exist in model)
post_summ(cjs, "p")
# do this by chain
post_summ(cjs, "p", by_chain = TRUE)
# calculate Rhat and Neff diagnostic summaries as well
# multiple node names too
post_summ(cjs, c("b0", "p"), Rhat = TRUE, neff = TRUE)
# calculate Monte Carlo SE for mean and quantiles, with rounding
post_summ(cjs, "p", mcse = TRUE, digits = 3)
# summarize different quantiles: median and central 80%
post_summ(cjs, "p", probs = c(0.5, 0.1, 0.9))
``` |

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