Description Usage Arguments Value References See Also Examples
Compute effective sample size estimates for quantile estimates of a single variable.
1 2 3 4 5 6 7 8 9 10 11 12  ess_quantile(x, probs = c(0.05, 0.95), ...)
## Default S3 method:
ess_quantile(x, probs = c(0.05, 0.95), names = TRUE, ...)
## S3 method for class 'rvar'
ess_quantile(x, probs = c(0.05, 0.95), names = TRUE, ...)
ess_median(x, ...)
## Default S3 method:
ess_mean(x, ...)

x 
(multiple options) One of:

probs 
(numeric vector) Probabilities in 
... 
Arguments passed to individual methods (if applicable). 
names 
(logical) Should the result have a 
If the input is an array,
returns a numeric vector with one element per quantile. If any of the draws is
nonfinite, that is, NA
, NaN
, Inf
, or Inf
, the returned output will
be a vector of (numeric) NA
values. Also, if all draws of a variable are
the same (constant), the returned output will be a vector of (numeric) NA
values 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
and length(probs) == 1
, 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. If length(probs) > 1
, the first dimension of the
result indexes the input probabilities; i.e. the result has dimension
c(length(probs), dim(x))
.
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and
PaulChristian Bürkner (2019). Ranknormalization, folding, and
localization: An improved Rhat for assessing convergence of
MCMC. arXiv preprint arXiv:1903.08008
.
Other diagnostics:
ess_basic()
,
ess_bulk()
,
ess_sd()
,
ess_tail()
,
mcse_mean()
,
mcse_quantile()
,
mcse_sd()
,
rhat_basic()
,
rhat()
,
rstar()
1 2 3 4 5  mu < extract_variable_matrix(example_draws(), "mu")
ess_quantile(mu, probs = c(0.1, 0.9))
d < as_draws_rvars(example_draws("multi_normal"))
ess_quantile(d$mu, probs = c(0.1, 0.9))

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