diagnostic_posterior {bayestestR} | R Documentation |
Posteriors Sampling Diagnostic
Description
Extract diagnostic metrics (Effective Sample Size (ESS
), Rhat
and Monte
Carlo Standard Error MCSE
).
Usage
diagnostic_posterior(posterior, ...)
## Default S3 method:
diagnostic_posterior(posterior, diagnostic = c("ESS", "Rhat"), ...)
## S3 method for class 'stanreg'
diagnostic_posterior(
posterior,
diagnostic = "all",
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
...
)
## S3 method for class 'brmsfit'
diagnostic_posterior(
posterior,
diagnostic = "all",
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
...
)
Arguments
posterior |
A |
... |
Currently not used. |
diagnostic |
Diagnostic metrics to compute. Character (vector) or list
with one or more of these options: |
effects |
Should parameters for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated. |
component |
Should all predictor variables, predictor variables for the conditional model, the zero-inflated part of the model, the dispersion term or the instrumental variables be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variable (so called fixed-effects regressions). May be abbreviated. Note that the conditional component is also called count or mean component, depending on the model. |
parameters |
Regular expression pattern that describes the parameters that should be returned. |
Details
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".
References
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., and 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.
Examples
# rstanarm models
# -----------------------------------------------
model <- suppressWarnings(
rstanarm::stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
)
diagnostic_posterior(model)
# brms models
# -----------------------------------------------
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
diagnostic_posterior(model)