mcse: Monte-Carlo Standard Error (MCSE)

In DominiqueMakowski/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 = "fixed", component = "location", parameters = NULL, ...)

Arguments

model	A stanreg, stanfit, brmsfit, blavaan, or MCMCglmm object.
...	Currently not used.
effects	Should results for fixed effects ("fixed", the default), random effects ("random") or both ("⁠all"⁠) be returned? Only applies to mixed models. May be abbreviated.
component	Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, etc. See details in section Model Components. May be abbreviated. Note that the conditional component also refers to the count or mean component - names may differ, depending on the modeling package. There are three convenient shortcuts (not applicable to all model classes): component = "all" returns all possible parameters. If component = "location", location parameters such as conditional, zero_inflated, smooth_terms, or instruments are returned (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters). For component = "distributional" (or "auxiliary"), components like sigma, dispersion, beta or precision (and other auxiliary parameters) are returned.
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”.

Model components

Possible values for the component argument depend on the model class. Following are valid options:

• "all": returns all model components, applies to all models, but will only have an effect for models with more than just the conditional model component.

• "conditional": only returns the conditional component, i.e. "fixed effects" terms from the model. Will only have an effect for models with more than just the conditional model component.

• "smooth_terms": returns smooth terms, only applies to GAMs (or similar models that may contain smooth terms).

• "zero_inflated" (or "zi"): returns the zero-inflation component.

• "location": returns location parameters such as conditional, zero_inflated, or smooth_terms (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters).

• "distributional" (or "auxiliary"): components like sigma, dispersion, beta or precision (and other auxiliary parameters) are returned.

For models of class brmsfit (package brms), even more options are possible for the component argument, which are not all documented in detail here. See also ?insight::find_parameters.

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)

DominiqueMakowski/bayestestR documentation built on March 29, 2025, 4:17 p.m.