Summary method for salmonIPMfit objects

Summarize the posterior distributions of estimated parameters and derived quantities using the MCMC draws in a salmonIPMfit object.

Usage

# S3 method for class 'salmonIPMfit'
summary(
  object,
  pars = "hyper",
  include = TRUE,
  probs = c(0.05, 0.5, 0.95),
  funs = list(),
  .cores = 1
)

Arguments

object

An object of class salmonIPMfit.

pars

A character vector specifying (hyper)parameters, states, and/or quantities of interest ("parameters") to summarize. Parameters can be explicitly named or one or more shortcuts can be used to specify hierarchical levels of parameters; see stan_pars() for details. The default is "hyper", i.e. the top-level hyperparameters that are given priors.

include

Logical scalar defaulting to TRUE indicating whether to include or exclude the parameters given by pars. If FALSE, only entire multidimensional parameters can be excluded, rather than particular elements of them.

probs

A numeric vector of posterior quantiles to return. The default is c(0.05, 0.5, 0.95), i.e. the median and 90% credible interval.

funs

Named list of summary or diagnostic functions. The provided names will be used as the names of the columns in the result unless a function returns a named vector, in which case the latter names are used for the corresponding columns. The functions can be specified in any format supported by rlang::as_function(). Passed to the ... argument of summarize_draws().

.cores

The number of cores to use for computing summaries for different variables in parallel. The default is .cores = 1, in which case no parallelization is used.

Value

A data frame whose first column contains the variable names and the remaining columns contain summary statistics and diagnostics.

Details

Internally the posterior samples are converted to a draws object and summarize_draws() is called to compute the summary statistics and diagnostics. See that function's documentation for available diagnostic functions, syntax and examples. The default summaries are mean, sd, and ~quantile(.x, probs), and the default diagnostics are the bulk effective sample size ess_bulk() and the Gelman-Rubin potential scale reduction factor rhat(). If other summary or diagnostic functions are specified via funs, they override the defaults rather than augmenting them.

If pars includes correlation matrices, only the lower triangular elements are returned. This avoids redundant summary output as well as false positive diagnostic results such as bulk-ESS and Rhat being NaN for the diagonal elements.

See also

draws, draws_summary(), diagnostics, salmonIPMfit

Examples

# <under construction>