View source: R/pareto_smooth.R
pareto_khat  R Documentation 
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. (2024).
pareto_khat(x, ...)
## Default S3 method:
pareto_khat(
x,
tail = c("both", "right", "left"),
r_eff = NULL,
ndraws_tail = NULL,
verbose = FALSE,
are_log_weights = FALSE,
...
)
## S3 method for class 'rvar'
pareto_khat(x, ...)
x 
(multiple options) One of:

... 
Arguments passed to individual methods (if applicable). 
tail 
(string) The tail to diagnose/smooth:
The default is 
r_eff 
(numeric) relative effective sample size estimate. If

ndraws_tail 
(numeric) number of draws for the tail. If

verbose 
(logical) Should diagnostic messages be printed? If

are_log_weights 
(logical) Are the draws log weights? Default is

If the input is an array, returns a single numeric value. If any of the draws
is nonfinite, that is, NA
, NaN
, Inf
, or Inf
, the returned output
will be (numeric) NA
. Also, if all draws within any of the chains of a
variable are the same (constant), the returned output will be (numeric) NA
as well. The reason for the latter is that, for constant draws, we cannot
distinguish between variables that are supposed to be constant (e.g., a
diagonal element of a correlation matrix is always 1) or variables that just
happened to be constant because of a failure of convergence or other problems
in the sampling process.
If the input is an rvar
, returns an array of the same dimensions as the
rvar
, where each element is equal to the value that would be returned by
passing the draws array for that element of the rvar
to this function.
Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao and Jonah Gabry (2024). Pareto Smoothed Importance Sampling. Journal of Machine Learning Research, 25(72):158. PDF
pareto_diags
for additional related diagnostics, and
pareto_smooth
for Pareto smoothed draws.
Other diagnostics:
ess_basic()
,
ess_bulk()
,
ess_quantile()
,
ess_sd()
,
ess_tail()
,
mcse_mean()
,
mcse_quantile()
,
mcse_sd()
,
pareto_diags()
,
rhat()
,
rhat_basic()
,
rhat_nested()
,
rstar()
mu < extract_variable_matrix(example_draws(), "mu")
pareto_khat(mu)
d < as_draws_rvars(example_draws("multi_normal"))
pareto_khat(d$Sigma)
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