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 = "fixed",
component = "location",
parameters = NULL,
...
)Arguments
- posterior
A
stanreg,stanfit,brmsfit, orblavaanobject.- ...
Currently only used for models of class
brmsfit, where avariableargument can be used, which is directly passed to theas.data.frame()method (i.e.,as.data.frame(x, variable = variable)).- diagnostic
Diagnostic metrics to compute. Character (vector) or list with one or more of these options:
"ESS","Rhat","MCSE"or"all".- effects
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 withsd_orcor_)."grouplevel"returns random effects group level estimates, i.e. those parameters that start withr_."random"returns both"random_variance"and"grouplevel"."all"returns fixed effects and random effects variances."full"returns all parameters.
- 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). 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 asconditional,zero_inflated,smooth_terms, orinstrumentsare returned (everything that are fixed or random effects - depending on theeffectsargument - but no auxiliary parameters).For
component = "distributional"(or"auxiliary"), components likesigma,dispersion,betaorprecision(and other auxiliary parameters) are returned.
- 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".
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 asconditional,zero_inflated, orsmooth_terms(everything that are fixed or random effects - depending on theeffectsargument - but no auxiliary parameters)."distributional"(or"auxiliary"): components likesigma,dispersion,betaorprecision(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
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
# \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 6e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.019 seconds (Warm-up)
#> Chain 1: 0.019 seconds (Sampling)
#> Chain 1: 0.038 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 3e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.019 seconds (Warm-up)
#> Chain 2: 0.018 seconds (Sampling)
#> Chain 2: 0.037 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 3e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.019 seconds (Warm-up)
#> Chain 3: 0.019 seconds (Sampling)
#> Chain 3: 0.038 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 3e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.018 seconds (Warm-up)
#> Chain 4: 0.016 seconds (Sampling)
#> Chain 4: 0.034 seconds (Total)
#> 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
# }