Compute effective sample size estimates for quantile estimates of a single variable.

- x
(multiple options) One of:

A matrix of draws for a single variable (iterations x chains). See

`extract_variable_matrix()`

.An

`rvar`

.

- probs
(numeric vector) Probabilities in

`[0, 1]`

.- ...
Arguments passed to individual methods (if applicable).

- names
(logical) Should the result have a

`names`

attribute? The default is`TRUE`

, but use`FALSE`

for improved speed if there are many values in`probs`

.

If the input is an array,
returns a numeric vector with one element per quantile. If any of the draws is
non-finite, 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
Paul-Christian Bürkner (2021). Rank-normalization, folding, and
localization: An improved R-hat for assessing convergence of
MCMC (with discussion). *Bayesian Data Analysis*. 16(2), 667-–718.
doi:10.1214/20-BA1221

Other diagnostics:
`ess_basic()`

,
`ess_bulk()`

,
`ess_sd()`

,
`ess_tail()`

,
`mcse_mean()`

,
`mcse_quantile()`

,
`mcse_sd()`

,
`pareto_diags()`

,
`pareto_khat()`

,
`rhat()`

,
`rhat_basic()`

,
`rhat_nested()`

,
`rstar()`

```
mu <- extract_variable_matrix(example_draws(), "mu")
ess_quantile(mu, probs = c(0.1, 0.9))
#> ess_q10 ess_q90
#> 300.6674 325.0324
d <- as_draws_rvars(example_draws("multi_normal"))
ess_quantile(d$mu, probs = c(0.1, 0.9))
#> [,1] [,2] [,3]
#> ess_q10 383.4835 468.2163 340.6056
#> ess_q90 389.0418 419.6722 271.5482
```