Convert posterior samples for MCMC diagnostics

Coefficient matrices retain their chain mapping in an attribute. Sampled quantities use their .chain, .iteration, and .draw columns. A plain matrix without a mapping is treated as one chain; chain boundaries are never inferred from its number of rows. Filtering must leave the same consecutive iterations in every chain. Missing observations remain missing variables.

Usage

as_mcmc_draws(
  x,
  format = c("draws_array", "draws_df", "mcmc.list", "array"),
  value = NULL
)

Arguments

x: A coefficient matrix from post_draws() or a table from fitted_samples(), posterior_samples(), smooth_samples(), or derivative_samples().
format: One of "draws_array", "draws_df", "mcmc.list", or "array". The first two require posterior; "mcmc.list" requires coda.
value: Column containing sampled values. By default the appropriate column is selected from .fitted, .response, .value, or .derivative.

Value

An iteration by chain by variable array, a posterior draws object, or a coda list of chains. Variable names identify sampled quantities and their observation rows, parameters, or smooths.