Extract diagnostic metrics (Effective Sample Size (ESS), Rhat and Monte
Carlo Standard Error MCSE).
diagnostic_posterior(posterior, ...)
# Default S3 method
diagnostic_posterior(posterior, diagnostic = c("ESS", "Rhat"), ...)
# S3 method for class 'stanreg'
diagnostic_posterior(
posterior,
diagnostic = "all",
effects = "fixed",
component = "location",
parameters = NULL,
...
)A stanreg, stanfit, brmsfit, or blavaan object.
Currently only used for models of class brmsfit, where a variable
argument can be used, which is directly passed to the as.data.frame()
method (i.e., as.data.frame(x, variable = variable)).
Diagnostic metrics to compute. Character (vector) or list
with one or more of these options: "ESS", "Rhat", "MCSE" or "all".
Should variables for fixed effects ("fixed"), random effects
("random") or both ("all") be returned? Only applies to mixed models. May
be abbreviated.
For models of from packages brms or rstanarm there are additional options:
"fixed" returns fixed effects.
"random_variance" return random effects parameters (variance and
correlation components, e.g. those parameters that start with sd_ or
cor_).
"grouplevel" returns random effects group level estimates, i.e. those
parameters that start with r_.
"random" returns both "random_variance" and "grouplevel".
"all" returns fixed effects and random effects variances.
"full" returns all parameters.
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). 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.
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".
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.
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.
# \donttest{
# rstanarm models
# -----------------------------------------------
model <- suppressWarnings(
rstanarm::stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
)
diagnostic_posterior(model)
#> Parameter Rhat ESS MCSE
#> 1 (Intercept) 1.026862 148.0908 0.46193224
#> 2 gear 1.026872 167.9762 0.07793465
#> 3 wt 1.007318 165.5256 0.05915750
# brms models
# -----------------------------------------------
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1: 0.062 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 8e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
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#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 7e-06 seconds
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#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 7e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
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#> Chain 4:
diagnostic_posterior(model)
#> Parameter Rhat ESS MCSE
#> 1 b_Intercept 0.9992935 5206.918 0.02449703
#> 2 b_cyl 1.0035762 1876.678 0.01017518
#> 3 b_wt 1.0033650 1924.503 0.01830200
# }