pareto_khat: Pareto khat diagnostic

In posterior: Tools for Working with Posterior Distributions

Pareto khat diagnostic

Description

Estimate Pareto k value by fitting a Generalized Pareto Distribution to one or two tails of x. This can be used to estimate the number of fractional moments that is useful for convergence diagnostics. For further details see Vehtari et al. (2022).

Usage

pareto_khat(x, ...)

## Default S3 method:
pareto_khat(
  x,
  tail = c("both", "right", "left"),
  r_eff = NULL,
  ndraws_tail = NULL,
  verbose = FALSE,
  ...
)

## S3 method for class 'rvar'
pareto_khat(x, ...)

Arguments

x	(multiple options) One of: A matrix of draws for a single variable (iterations x chains). See extract_variable_matrix(). An rvar.
...	Arguments passed to individual methods (if applicable).
tail	(string) The tail to diagnose/smooth: "right": diagnose/smooth only the right (upper) tail "left": diagnose/smooth only the left (lower) tail "both": diagnose/smooth both tails and return the maximum k-hat value The default is "both".
r_eff	(numeric) relative effective sample size estimate. If r_eff is omitted, it will be calculated assuming the draws are from MCMC.
ndraws_tail	(numeric) number of draws for the tail. If ndraws_tail is not specified, it will be calculated as ceiling(3 * sqrt(length(x) / r_eff)) if length(x) > 225 and length(x) / 5 otherwise (see Appendix H in Vehtari et al. (2022)).
verbose	(logical) Should diagnostic messages be printed? If TRUE, messages related to Pareto diagnostics will be printed. Default is FALSE.

Value

khat estimated Generalized Pareto Distribution shape parameter k

References

Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao and Jonah Gabry (2022). Pareto Smoothed Importance Sampling. arxiv:arXiv:1507.02646

Examples

mu <- extract_variable_matrix(example_draws(), "mu")
pareto_khat(mu)

d <- as_draws_rvars(example_draws("multi_normal"))
pareto_khat(d$Sigma)

posterior documentation built on Nov. 2, 2023, 5:56 p.m.