# mcse_quantile: Monte Carlo standard error for quantiles In posterior: Tools for Working with Posterior Distributions

## Description

Compute Monte Carlo standard errors for quantile estimates of a single variable.

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```mcse_quantile(x, probs = c(0.05, 0.95), ...) ## Default S3 method: mcse_quantile(x, probs = c(0.05, 0.95), names = TRUE, ...) ## S3 method for class 'rvar' mcse_quantile(x, probs = c(0.05, 0.95), names = TRUE, ...) mcse_median(x, ...) ```

## Arguments

 `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`.

## Value

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))`.

## References

Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner (2019). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. arXiv preprint `arXiv:1903.08008`.

Other diagnostics: `ess_basic()`, `ess_bulk()`, `ess_quantile()`, `ess_sd()`, `ess_tail()`, `mcse_mean()`, `mcse_sd()`, `rhat_basic()`, `rhat()`, `rstar()`
 ```1 2 3 4 5``` ```mu <- extract_variable_matrix(example_draws(), "mu") mcse_quantile(mu, probs = c(0.1, 0.9)) d <- as_draws_rvars(example_draws("multi_normal")) mcse_quantile(d\$mu) ```