This function returns the Monte Carlo Standard Error (MCSE).

## Arguments

- model
A

`stanreg`

,`stanfit`

,`brmsfit`

,`blavaan`

, or`MCMCglmm`

object.- ...
Currently not used.

- effects
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.

- component
Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models.

- parameters
Regular expression pattern that describes the parameters that should be returned. Meta-parameters (like

`lp__`

or`prior_`

) are filtered by default, so only parameters that typically appear in the`summary()`

are returned. Use`parameters`

to select specific parameters for the output.

## Details

**Monte Carlo Standard Error (MCSE)** is another measure of
accuracy of the chains. It is defined as standard deviation of the chains
divided by their effective sample size (the formula for `mcse()`

is
from Kruschke 2015, p. 187). The MCSE “provides a quantitative
suggestion of how big the estimation noise is”.

## References

Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.

## Examples

```
# \dontrun{
library(bayestestR)
library(rstanarm)
model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
mcse(model)
#> Parameter MCSE
#> 1 (Intercept) 0.14420893
#> 2 wt 0.03564543
#> 3 am 0.06916278
# }
```