Compute the Monte Carlo standard error for the mean (expectation) of a single variable.

```
mcse_mean(x, ...)
# S3 method for default
mcse_mean(x, ...)
# S3 method for rvar
mcse_mean(x, ...)
```

- 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).

If the input is an array, returns a single numeric value. If any of the draws
is non-finite, 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.

Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and
Donald B. Rubin (2013). *Bayesian Data Analysis, Third Edition*. Chapman and
Hall/CRC.

Other diagnostics:
`ess_basic()`

,
`ess_bulk()`

,
`ess_quantile()`

,
`ess_sd()`

,
`ess_tail()`

,
`mcse_quantile()`

,
`mcse_sd()`

,
`rhat_basic()`

,
`rhat()`

,
`rstar()`

```
mu <- extract_variable_matrix(example_draws(), "mu")
mcse_mean(mu)
#> [1] 0.1504394
d <- as_draws_rvars(example_draws("multi_normal"))
mcse_mean(d$Sigma)
#> [,1] [,2] [,3]
#> [1,] 0.006331065 0.009478331 0.01283981
#> [2,] 0.009478331 0.019579138 0.02346471
#> [3,] 0.012839814 0.023464711 0.03748603
```