##### https://github.com/cran/bayestestR

Tip revision:

**645c10f110efae44c5ac60025664cd27f2b46a87**authored by**Dominique Makowski**on**26 March 2020, 05:10 UTC****version 0.5.3** Tip revision:

**645c10f** mcse.Rd

```
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mcse.R
\name{mcse}
\alias{mcse}
\alias{mcse.stanreg}
\title{Monte-Carlo Standard Error (MCSE)}
\usage{
mcse(model, ...)
\method{mcse}{stanreg}(model, effects = c("fixed", "random", "all"), parameters = NULL, ...)
}
\arguments{
\item{model}{A \code{stanreg}, \code{stanfit}, or \code{brmsfit} object.}
\item{...}{Currently not used.}
\item{effects}{Should results for fixed effects, random effects or both be returned?
Only applies to mixed models. May be abbreviated.}
\item{parameters}{Regular expression pattern that describes the parameters that
should be returned. Meta-parameters (like \code{lp__} or \code{prior_}) are
filtered by default, so only parameters that typically appear in the
\code{summary()} are returned. Use \code{parameters} to select specific parameters
for the output.}
}
\description{
This function returns the Monte Carlo Standard Error (MCSE).
}
\details{
\strong{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 \code{mcse()} is
from Kruschke 2015, p. 187). The MCSE \dQuote{provides a quantitative
suggestion of how big the estimation noise is}.
}
\examples{
\dontrun{
library(bayestestR)
library(rstanarm)
model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
mcse(model)
}
}
\references{
Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
}
```