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

mcse(model, ...)
# S3 method for stanreg
mcse(
model,
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
...
)

## Arguments

model |
A `stanreg` , `stanfit` , or `brmsfit` 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