Extract diagnostic metrics (Effective Sample Size (ESS), Rhat and Monte
Carlo Standard Error MCSE).
diagnostic_posterior(posteriors, diagnostic = c("ESS", "Rhat"), ...) # S3 method for stanreg diagnostic_posterior( posteriors, diagnostic = "all", effects = c("fixed", "random", "all"), component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"), parameters = NULL, ... ) # S3 method for stanmvreg diagnostic_posterior( posteriors, diagnostic = "all", effects = c("fixed", "random", "all"), parameters = NULL, ... ) # S3 method for brmsfit diagnostic_posterior( posteriors, diagnostic = "all", effects = c("fixed", "random", "all"), component = c("conditional", "zi", "zero_inflated", "all"), parameters = NULL, ... )
| posteriors | A stanreg or brms model. |
|---|---|
| diagnostic | Diagnostic metrics to compute. Character (vector) or list
with one or more of these options: |
| ... | Currently not used. |
| effects | Should parameters for fixed effects, random effects or both 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, the instrumental variables or marginal effects be returned? Applies
to models with zero-inflated and/or dispersion formula, or to models with
instrumental variables (so called fixed-effects regressions), or models
with marginal effects from mfx. May be abbreviated. Note that the
conditional component is also called count or mean
component, depending on the model. There are three convenient shortcuts:
|
| parameters | Regular expression pattern that describes the parameters that should be returned. |
Effective Sample (ESS) should be as large as possible, although for
most applications, an effective sample size greater than 1000 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).
Rhat should be the closest to 1. It should not be larger than 1.1
(Gelman and Rubin, 1992) or 1.01 (Vehtari et al., 2019). The
split Rhat statistic quantifies the consistency of an ensemble of Markov
chains.
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”.
Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 7(4), 457-472.
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., \& Bürkner, P. C. (2019). Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of MCMC. arXiv preprint arXiv:1903.08008.
Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
if (FALSE) { # rstanarm models # ----------------------------------------------- if (require("rstanarm", quietly = TRUE)) { model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0) diagnostic_posterior(model) } # brms models # ----------------------------------------------- if (require("brms", quietly = TRUE)) { model <- brms::brm(mpg ~ wt + cyl, data = mtcars) diagnostic_posterior(model) } }