describe_posterior: Describe Posterior Distributions

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

Describe Posterior Distributions

Description

Compute indices relevant to describe and characterize the posterior distributions.

Usage

describe_posterior(posterior, ...)

## S3 method for class 'numeric'
describe_posterior(
  posterior,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "eti",
  test = c("p_direction", "rope"),
  rope_range = "default",
  rope_ci = 0.95,
  keep_iterations = FALSE,
  bf_prior = NULL,
  BF = 1,
  verbose = TRUE,
  ...
)

## S3 method for class 'data.frame'
describe_posterior(
  posterior,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "eti",
  test = c("p_direction", "rope"),
  rope_range = "default",
  rope_ci = 0.95,
  keep_iterations = FALSE,
  bf_prior = NULL,
  BF = 1,
  rvar_col = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'stanreg'
describe_posterior(
  posterior,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "eti",
  test = c("p_direction", "rope"),
  rope_range = "default",
  rope_ci = 0.95,
  keep_iterations = FALSE,
  bf_prior = NULL,
  diagnostic = c("ESS", "Rhat"),
  priors = FALSE,
  effects = c("fixed", "random", "all"),
  component = c("location", "all", "conditional", "smooth_terms", "sigma",
    "distributional", "auxiliary"),
  parameters = NULL,
  BF = 1,
  verbose = TRUE,
  ...
)

## S3 method for class 'brmsfit'
describe_posterior(
  posterior,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "eti",
  test = c("p_direction", "rope"),
  rope_range = "default",
  rope_ci = 0.95,
  keep_iterations = FALSE,
  bf_prior = NULL,
  diagnostic = c("ESS", "Rhat"),
  effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all", "location",
    "distributional", "auxiliary"),
  parameters = NULL,
  BF = 1,
  priors = FALSE,
  verbose = TRUE,
  ...
)

Arguments

posterior	A vector, data frame or model of posterior draws. bayestestR supports a wide range of models (see methods("describe_posterior")) and not all of those are documented in the 'Usage' section, because methods for other classes mostly resemble the arguments of the .numeric method.
...	Additional arguments to be passed to or from methods.
centrality	The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: "median", "mean", "MAP" (see map_estimate()), "trimmed" (which is just mean(x, trim = threshold)), "mode" or "all".
dispersion	Logical, if TRUE, computes indices of dispersion related to the estimate(s) (SD and MAD for mean and median, respectively). Dispersion is not available for "MAP" or "mode" centrality indices.
ci	Value or vector of probability of the CI (between 0 and 1) to be estimated. Default to 0.95 (⁠95%⁠).
ci_method	The type of index used for Credible Interval. Can be "ETI" (default, see eti()), "HDI" (see hdi()), "BCI" (see bci()), "SPI" (see spi()), or "SI" (see si()).
test	The indices of effect existence to compute. Character (vector) or list with one or more of these options: "p_direction" (or "pd"), "rope", "p_map", "equivalence_test" (or "equitest"), "bayesfactor" (or "bf") or "all" to compute all tests. For each "test", the corresponding bayestestR function is called (e.g. rope() or p_direction()) and its results included in the summary output.
rope_range	ROPE's lower and higher bounds. Should be a vector of two values (e.g., c(-0.1, 0.1)), "default" or a list of numeric vectors of the same length as numbers of parameters. If "default", the bounds are set to x +- 0.1*SD(response).
rope_ci	The Credible Interval (CI) probability, corresponding to the proportion of HDI, to use for the percentage in ROPE.
keep_iterations	If TRUE, will keep all iterations (draws) of bootstrapped or Bayesian models. They will be added as additional columns named ⁠iter_1, iter_2, ...⁠. You can reshape them to a long format by running reshape_iterations().
bf_prior	Distribution representing a prior for the computation of Bayes factors / SI. Used if the input is a posterior, otherwise (in the case of models) ignored.
BF	The amount of support required to be included in the support interval.
verbose	Toggle off warnings.
rvar_col	A single character - the name of an rvar column in the data frame to be processed. See example in p_direction().
diagnostic	Diagnostic metrics to compute. Character (vector) or list with one or more of these options: "ESS", "Rhat", "MCSE" or "all".
priors	Add the prior used for each parameter.
effects	Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
component	Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models.
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

One or more components of point estimates (like posterior mean or median), intervals and tests can be omitted from the summary output by setting the related argument to NULL. For example, test = NULL and centrality = NULL would only return the HDI (or CI).

References

• Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and Lüdecke, D. (2019). Indices of Effect Existence and Significance in the Bayesian Framework. Frontiers in Psychology 2019;10:2767. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3389/fpsyg.2019.02767")}

• Region of Practical Equivalence (ROPE)

• Bayes factors

Examples

library(bayestestR)

if (require("logspline")) {
  x <- rnorm(1000)
  describe_posterior(x, verbose = FALSE)
  describe_posterior(x,
    centrality = "all",
    dispersion = TRUE,
    test = "all",
    verbose = FALSE
  )
  describe_posterior(x, ci = c(0.80, 0.90), verbose = FALSE)

  df <- data.frame(replicate(4, rnorm(100)))
  describe_posterior(df, verbose = FALSE)
  describe_posterior(
    df,
    centrality = "all",
    dispersion = TRUE,
    test = "all",
    verbose = FALSE
  )
  describe_posterior(df, ci = c(0.80, 0.90), verbose = FALSE)

  df <- data.frame(replicate(4, rnorm(20)))
  head(reshape_iterations(
    describe_posterior(df, keep_iterations = TRUE, verbose = FALSE)
  ))
}

# rstanarm models
# -----------------------------------------------
if (require("rstanarm") && require("emmeans")) {
  model <- suppressWarnings(
    stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
  )
  describe_posterior(model)
  describe_posterior(model, centrality = "all", dispersion = TRUE, test = "all")
  describe_posterior(model, ci = c(0.80, 0.90))
  describe_posterior(model, rope_range = list(c(-10, 5), c(-0.2, 0.2), "default"))

  # emmeans estimates
  # -----------------------------------------------
  describe_posterior(emtrends(model, ~1, "wt"))
}

# BayesFactor objects
# -----------------------------------------------
if (require("BayesFactor")) {
  bf <- ttestBF(x = rnorm(100, 1, 1))
  describe_posterior(bf)
  describe_posterior(bf, centrality = "all", dispersion = TRUE, test = "all")
  describe_posterior(bf, ci = c(0.80, 0.90))
}

bayestestR documentation built on Oct. 17, 2024, 5:07 p.m.