effective_sample: Effective Sample Size (ESS)

In DominiqueMakowski/bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

Effective Sample Size (ESS)

Description

Effective Sample Size (ESS) is a measure of how much independent information there is in autocorrelated chains. It is used to assess the quality of MCMC samples. A higher ESS indicates more reliable estimates. For most applications, an effective sample size greater than 1,000 is sufficient for stable estimates (Bürkner, 2017). This function returns the effective sample size (ESS) for various Bayesian model objects. For brmsfit objects, the returned ESS corresponds to the bulk-ESS (and the tail-ESS is also returned).

Usage

effective_sample(model, ...)

## S3 method for class 'brmsfit'
effective_sample(
  model,
  effects = "fixed",
  component = "conditional",
  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

• 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 “how much independent information there is in autocorrelated chains” (Kruschke 2015, p182-3).

• Bulk-ESS is useful as a diagnostic for the sampling efficiency in the bulk of the posterior. It is defined as the effective sample size for rank normalized values using split chains. It can be interpreted as the reliability of indices of central tendency (mean, median, etc.).

• Tail-ESS is useful as a diagnostic for the sampling efficiency in the tails of the posterior. It is defined as the minimum of the effective sample sizes for 5% and 95% quantiles. It can be interpreted as the reliability of indices that depend on the tails of the distribution (e.g., credible intervals, tail probabilities, etc.).

Value

A data frame with two columns: Parameter name and effective sample size (ESS).

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.

• Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1-28

• Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P.-C. (2021). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. Bayesian Analysis, 16(2), 667-718.

Examples

model <- suppressWarnings(rstanarm::stan_glm(
  mpg ~ wt + gear,
  data = mtcars,
  chains = 2,
  iter = 200,
  refresh = 0
))
effective_sample(model)

model <- suppressWarnings(brms::brm(
  mpg ~ wt,
  data = mtcars,
  chains = 2,
  iter = 200,
  refresh = 0
))
effective_sample(model)

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