mcse {bayestestR} | R Documentation |

## Monte-Carlo Standard Error (MCSE)

### Description

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

### Usage

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

### Arguments

`model` |
A |

`...` |
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 |

### 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

```
library(bayestestR)
model <- suppressWarnings(
rstanarm::stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
)
mcse(model)
```

*bayestestR*version 0.13.2 Index]