summary.mcmc_output: Summary Statistics of Posterior Samples

In bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

Summary Statistics of Posterior Samples

Description

This functions returns a data frame containing mean, standard deviations, standard errors, and effective sample size estimates for parameters and states.

Usage

## S3 method for class 'mcmc_output'
summary(
  object,
  return_se = FALSE,
  variable = "theta",
  probs = c(0.025, 0.975),
  times,
  states,
  use_times = TRUE,
  method = "sokal",
  ...
)

Arguments

object	Output from run_mcmc
return_se	if FALSE (default), computation of standard errors and effective sample sizes is omitted (as they can take considerable time for models with large number of states and time points).
variable	Are the summary statistics computed for either "theta" (default), "states", or "both"?
probs	A numeric vector defining the quantiles of interest. Default is c(0.025, 0.975).
times	A vector of indices. For states, for what time points the summaries should be computed? Default is all, ignored if variable = "theta".
states	A vector of indices. For what states the summaries should be computed?. Default is all, ignored if variable = "theta".
use_times	If TRUE (default), transforms the values of the time variable to match the ts attribute of the input to define. If FALSE, time is based on the indexing starting from 1.
method	Method for computing integrated autocorrelation time. Default is "sokal", other option is "geyer".
...	Ignored.

Details

For IS-MCMC two types of standard errors are reported. SE-IS can be regarded as the square root of independent IS variance, whereas SE corresponds to the square root of total asymptotic variance (see Remark 3 of Vihola et al. (2020)).

Value

If variable is "theta" or "states", a data.frame object. If "both", a list of two data frames.

References

Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492

Examples

data("negbin_model")
summary(negbin_model, return_se = TRUE, method = "geyer")
summary(negbin_model, times = c(1, 200), prob = c(0.05, 0.5, 0.95))

bssm documentation built on Nov. 2, 2023, 6:25 p.m.