Raw File
Tip revision: 2565fc870cd7f0a64d857ff89e682dc9344dc7c1 authored by Dominique Makowski on 12 February 2020, 04:10 UTC
version 0.5.2
Tip revision: 2565fc8
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mcse.R
\title{Monte-Carlo Standard Error (MCSE)}
mcse(model, ...)

\method{mcse}{stanreg}(model, effects = c("fixed", "random", "all"), parameters = NULL, ...)
\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.}
This function returns the Monte Carlo Standard Error (MCSE).
\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}.

model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
back to top