mcse: Monte-Carlo Standard Error (MCSE)

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

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 stanreg, stanfit, brmsfit, blavaan, or MCMCglmm 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

library(bayestestR)

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

bayestestR documentation built on Oct. 17, 2024, 5:07 p.m.