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Compute indices relevant to describe and characterize the posterior distributions.

Usage

describe_posterior(
  posteriors,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "hdi",
  test = c("p_direction", "rope"),
  rope_range = "default",
  rope_ci = 0.95,
  keep_iterations = FALSE,
  ...
)

# S3 method for numeric
describe_posterior(
  posteriors,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "hdi",
  test = c("p_direction", "rope"),
  rope_range = "default",
  rope_ci = 0.95,
  keep_iterations = FALSE,
  bf_prior = NULL,
  BF = 1,
  ...
)

# S3 method for stanreg
describe_posterior(
  posteriors,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "hdi",
  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,
  ...
)

# S3 method for stanmvreg
describe_posterior(
  posteriors,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "hdi",
  test = "p_direction",
  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,
  ...
)

# S3 method for brmsfit
describe_posterior(
  posteriors,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "hdi",
  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,
  ...
)

# S3 method for MCMCglmm
describe_posterior(
  posteriors,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "hdi",
  test = c("p_direction", "rope"),
  rope_range = "default",
  rope_ci = 0.95,
  keep_iterations = FALSE,
  diagnostic = "ESS",
  parameters = NULL,
  ...
)

# S3 method for BFBayesFactor
describe_posterior(
  posteriors,
  centrality = "median",
  dispersion = FALSE,
  ci = 0.95,
  ci_method = "hdi",
  test = c("p_direction", "rope", "bf"),
  rope_range = "default",
  rope_ci = 0.95,
  keep_iterations = FALSE,
  priors = TRUE,
  verbose = TRUE,
  ...
)

Arguments

posteriors

A vector, data frame or model of posterior draws.

centrality

The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: "median", "mean", "MAP" or "all".

dispersion

Logical, if TRUE, computes indices of dispersion related to the estimate(s) (SD and MAD for mean and median, respectively).

ci

Value or vector of probability of the CI (between 0 and 1) to be estimated. Default to .95 (95%).

ci_method

The type of index used for Credible Interval. Can be "HDI" (default, see hdi()), "ETI" (see eti()), "BCI" (see bci()) 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 list of two values (e.g., c(-0.1, 0.1)) or "default". 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().

...

Additional arguments to be passed to or from methods.

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.

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.

verbose

Toggle off warnings.

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).

Examples

library(bayestestR)

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

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

  df <- data.frame(replicate(4, rnorm(20)))
  head(reshape_iterations(describe_posterior(df, keep_iterations = TRUE)))
}
#> Warning: Prior not specified! Please specify a prior (in the form 'prior = distribution_normal(1000, 0, 1)') to get meaningful results.
#> Warning: Bayes factors might not be precise.
#> For precise Bayes factors, sampling at least 40,000 posterior samples is recommended.
#> Warning: Prior not specified! Please specify priors (with column order matching 'posterior') to get meaningful results.
#> Warning: Bayes factors might not be precise.
#> For precise Bayes factors, sampling at least 40,000 posterior samples is recommended.
#>   Parameter      Median   CI    CI_low  CI_high   pd ROPE_CI ROPE_low ROPE_high
#> 1        X1  0.03082071 0.95 -2.683293 2.134393 0.50    0.95     -0.1       0.1
#> 2        X2  0.05796759 0.95 -2.965922 1.408350 0.50    0.95     -0.1       0.1
#> 3        X3  0.24120419 0.95 -2.782019 2.180062 0.55    0.95     -0.1       0.1
#> 4        X4 -0.16551758 0.95 -2.119502 1.587682 0.70    0.95     -0.1       0.1
#> 5        X1  0.03082071 0.95 -2.683293 2.134393 0.50    0.95     -0.1       0.1
#> 6        X2  0.05796759 0.95 -2.965922 1.408350 0.50    0.95     -0.1       0.1
#>   ROPE_Percentage iter_index iter_group iter_value
#> 1            0.05          1          1 -1.4905827
#> 2            0.05          2          1  0.9779056
#> 3            0.00          3          1 -0.5303282
#> 4            0.10          4          1 -0.4821490
#> 5            0.05          1          2 -1.2998224
#> 6            0.05          2          2 -0.7012259
if (FALSE) {
# rstanarm models
# -----------------------------------------------
if (require("rstanarm") && require("emmeans")) {
  model <- 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))

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

# brms models
# -----------------------------------------------
if (require("brms")) {
  model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
  describe_posterior(model)
  describe_posterior(model, centrality = "all", dispersion = TRUE, test = "all")
  describe_posterior(model, ci = c(0.80, 0.90))
}

# 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))
}
}