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Tip revision: e1fa15d202de277bb07e58bb3013557724072b2b authored by Dominique Makowski on 22 September 2019, 15:30 UTC
version 0.3.0
Tip revision: e1fa15d
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/effective_sample.R
\title{Effective Sample Size (ESS)}
effective_sample(model, ...)

\method{effective_sample}{brmsfit}(model, effects = c("fixed", "random",
  "all"), component = c("conditional", "zi", "zero_inflated", "all"),
  parameters = NULL, ...)

\method{effective_sample}{stanreg}(model, effects = c("fixed", "random",
  "all"), parameters = NULL, ...)

\method{effective_sample}{MCMCglmm}(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{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 \pkg{brms}-models.}

\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.}
A data frame with two columns: Parameter name and effective sample size (ESS).
This function returns the effective sample size (ESS).
\strong{Effective Sample (ESS)} should be as large as possible, altough for most applications, an effective sample size greater than 1,000 is sufficient for stable estimates (Bürkner, 2017). The ESS corresponds to the number of independent samples with the same estimation power as the N autocorrelated samples. It is is a measure of \dQuote{how much independent information there is in autocorrelated chains} (\emph{Kruschke 2015, p182-3}).
model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
  \item Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
  \item Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1-28
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