Describe Posterior Distributions — describe

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 = "fixed",
  component = "location",
  parameters = NULL,
  BF = 1,
  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", "p_significance" (or "ps"), "p_rope", "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 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.

component

Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, etc. 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.

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).

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 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.

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. doi:10.3389/fpsyg.2019.02767
Region of Practical Equivalence (ROPE)
Bayes factors

Examples

library(bayestestR)

x <- rnorm(1000)
describe_posterior(x, verbose = FALSE)
#> Summary of Posterior Distribution
#> 
#> Parameter | Median |        95% CI |     pd |          ROPE | % in ROPE
#> -----------------------------------------------------------------------
#> Posterior |  -0.05 | [-1.97, 2.03] | 52.20% | [-0.10, 0.10] |     8.63%
describe_posterior(x,
  centrality = "all",
  dispersion = TRUE,
  test = "all",
  verbose = FALSE
)
#> Summary of Posterior Distribution
#> 
#> Parameter | Median |  MAD |  Mean |   SD |   MAP |        95% CI | p (MAP)
#> --------------------------------------------------------------------------
#> Posterior |  -0.05 | 0.98 | -0.02 | 1.03 | -0.13 | [-1.97, 2.03] |   0.982
#> 
#> Parameter |     pd | p (ROPE) |   ps |          ROPE | % in ROPE
#> ----------------------------------------------------------------
#> Posterior | 52.20% |    0.082 | 0.48 | [-0.10, 0.10] |     8.63%
#> 
#> Parameter | Equivalence (ROPE) |   BF
#> -------------------------------------
#> Posterior |          Undecided | 1.00
describe_posterior(x, ci = c(0.80, 0.90), verbose = FALSE)
#> Summary of Posterior Distribution
#> 
#> Parameter | Median |        80% CI |        90% CI |     pd |          ROPE | % in ROPE
#> ---------------------------------------------------------------------------------------
#> Posterior |  -0.05 | [-1.31, 1.31] | [-1.62, 1.69] | 52.20% | [-0.10, 0.10] |     8.63%

df <- data.frame(replicate(4, rnorm(100)))
describe_posterior(df, verbose = FALSE)
#> Summary of Posterior Distribution
#> 
#> Parameter | Median |        95% CI |     pd |          ROPE | % in ROPE
#> -----------------------------------------------------------------------
#> X1        |  -0.12 | [-2.21, 1.86] | 56.00% | [-0.10, 0.10] |    10.64%
#> X2        |  -0.28 | [-2.06, 1.69] | 56.00% | [-0.10, 0.10] |     6.38%
#> X3        |  -0.12 | [-1.80, 1.66] | 56.00% | [-0.10, 0.10] |     4.26%
#> X4        |  -0.23 | [-2.19, 1.65] | 59.00% | [-0.10, 0.10] |     2.13%
describe_posterior(
  df,
  centrality = "all",
  dispersion = TRUE,
  test = "all",
  verbose = FALSE
)
#> Summary of Posterior Distribution
#> 
#> Parameter | Median |  MAD |  Mean |   SD |   MAP |        95% CI | p (MAP)
#> --------------------------------------------------------------------------
#> X1        |  -0.12 | 1.20 | -0.18 | 1.10 | -0.11 | [-2.21, 1.86] |   0.997
#> X2        |  -0.28 | 1.19 | -0.10 | 1.07 | -0.64 | [-2.06, 1.69] |   0.796
#> X3        |  -0.12 | 0.89 | -0.03 | 0.99 | -0.32 | [-1.80, 1.66] |   0.919
#> X4        |  -0.23 | 1.09 | -0.21 | 1.06 | -0.40 | [-2.19, 1.65] |   0.958
#> 
#> Parameter |     pd | p (ROPE) |   ps |          ROPE | % in ROPE
#> ----------------------------------------------------------------
#> X1        | 56.00% |    0.100 | 0.50 | [-0.10, 0.10] |    10.64%
#> X2        | 56.00% |    0.060 | 0.53 | [-0.10, 0.10] |     6.38%
#> X3        | 56.00% |    0.040 | 0.53 | [-0.10, 0.10] |     4.26%
#> X4        | 59.00% |    0.020 | 0.57 | [-0.10, 0.10] |     2.13%
#> 
#> Parameter | Equivalence (ROPE) |   BF
#> -------------------------------------
#> X1        |          Undecided | 1.00
#> X2        |          Undecided | 1.00
#> X3        |          Undecided | 1.00
#> X4        |          Undecided | 1.00
describe_posterior(df, ci = c(0.80, 0.90), verbose = FALSE)
#> Summary of Posterior Distribution
#> 
#> Parameter | Median |        80% CI |        90% CI |     pd |          ROPE | % in ROPE
#> ---------------------------------------------------------------------------------------
#> X1        |  -0.12 | [-1.58, 1.27] | [-1.89, 1.47] | 56.00% | [-0.10, 0.10] |    10.64%
#> X2        |  -0.28 | [-1.32, 1.51] | [-1.84, 1.64] | 56.00% | [-0.10, 0.10] |     6.38%
#> X3        |  -0.12 | [-1.27, 1.18] | [-1.54, 1.57] | 56.00% | [-0.10, 0.10] |     4.26%
#> X4        |  -0.23 | [-1.57, 1.13] | [-1.80, 1.41] | 59.00% | [-0.10, 0.10] |     2.13%

df <- data.frame(replicate(4, rnorm(20)))
head(reshape_iterations(
  describe_posterior(df, keep_iterations = TRUE, verbose = FALSE)
))
#> Summary of Posterior Distribution
#> 
#> Parameter | Median |        95% CI |     pd |          ROPE | % in ROPE
#> -----------------------------------------------------------------------
#> X1        |   0.31 | [-1.55, 1.59] | 70.00% | [-0.10, 0.10] |     5.56%
#> X2        |   0.12 | [-1.45, 1.55] | 50.00% | [-0.10, 0.10] |     5.56%
#> X3        |  -0.06 | [-2.77, 1.70] | 60.00% | [-0.10, 0.10] |    16.67%
#> X4        |   0.13 | [-1.67, 1.94] | 60.00% | [-0.10, 0.10] |    11.11%
#> X1        |   0.31 | [-1.55, 1.59] | 70.00% | [-0.10, 0.10] |     5.56%
#> X2        |   0.12 | [-1.45, 1.55] | 50.00% | [-0.10, 0.10] |     5.56%
#> 
#> Parameter | iter_index | iter_group | iter_value
#> ------------------------------------------------
#> X1        |       1.00 |       1.00 |       0.86
#> X2        |       2.00 |       1.00 |      -0.77
#> X3        |       3.00 |       1.00 |      -1.51
#> X4        |       4.00 |       1.00 |       1.32
#> X1        |       1.00 |       2.00 |       0.16
#> X2        |       2.00 |       2.00 |       0.74

# \donttest{
# rstanarm models
# -----------------------------------------------
model <- suppressWarnings(
  rstanarm::stan_glm(
    mpg ~ wt + gear,
    data = mtcars, chains = 2, iter = 200,
    refresh = 0
  )
)
describe_posterior(model)
#> Summary of Posterior Distribution 
#> 
#> Parameter   | Median |         95% CI |     pd |          ROPE | % in ROPE
#> --------------------------------------------------------------------------
#> (Intercept) |  39.04 | [29.35, 48.29] |   100% | [-0.60, 0.60] |        0%
#> wt          |  -5.43 | [-6.71, -4.23] |   100% | [-0.60, 0.60] |        0%
#> gear        |  -0.30 | [-1.86,  1.56] | 63.00% | [-0.60, 0.60] |    54.74%
#> 
#> Parameter   |  Rhat |    ESS
#> ----------------------------
#> (Intercept) | 1.002 | 169.00
#> wt          | 1.002 | 217.00
#> gear        | 0.997 | 211.00
describe_posterior(model, centrality = "all", dispersion = TRUE, test = "all")
#> Warning: Bayes factors might not be precise.
#>   For precise Bayes factors, sampling at least 40,000 posterior samples is
#>   recommended.
#> Summary of Posterior Distribution 
#> 
#> Parameter   | Median |  MAD |  Mean |   SD |   MAP |         95% CI | p (MAP)
#> -----------------------------------------------------------------------------
#> (Intercept) |  39.04 | 3.98 | 38.71 | 4.71 | 40.40 | [29.35, 48.29] |  < .001
#> wt          |  -5.43 | 0.69 | -5.48 | 0.68 | -5.17 | [-6.71, -4.23] |  < .001
#> gear        |  -0.30 | 0.84 | -0.28 | 0.89 | -0.20 | [-1.86,  1.56] |  0.977 
#> 
#> Parameter   |     pd | p (ROPE) |   ps |          ROPE | % in ROPE
#> ------------------------------------------------------------------
#> (Intercept) |   100% |   < .001 | 1.00 | [-0.60, 0.60] |        0%
#> wt          |   100% |   < .001 | 1.00 | [-0.60, 0.60] |        0%
#> gear        | 63.00% |   0.520  | 0.35 | [-0.60, 0.60] |    54.74%
#> 
#> Parameter   | Equivalence (ROPE) |       BF |  Rhat |    ESS
#> ------------------------------------------------------------
#> (Intercept) |           Rejected | 6.33e+03 | 1.002 | 169.00
#> wt          |           Rejected | 4.88e+04 | 1.002 | 217.00
#> gear        |          Undecided |    0.044 | 0.997 | 211.00
describe_posterior(model, ci = c(0.80, 0.90))
#> Summary of Posterior Distribution 
#> 
#> Parameter   | Median |         80% CI |         90% CI |     pd |          ROPE
#> -------------------------------------------------------------------------------
#> (Intercept) |  39.04 | [33.38, 44.21] | [30.79, 46.08] |   100% | [-0.60, 0.60]
#> wt          |  -5.43 | [-6.36, -4.64] | [-6.66, -4.40] |   100% | [-0.60, 0.60]
#> gear        |  -0.30 | [-1.24,  0.74] | [-1.57,  1.30] | 63.00% | [-0.60, 0.60]
#> 
#> Parameter   | % in ROPE |  Rhat |    ESS
#> ----------------------------------------
#> (Intercept) |        0% | 1.002 | 169.00
#> wt          |        0% | 1.002 | 217.00
#> gear        |    54.74% | 0.997 | 211.00
describe_posterior(model, rope_range = list(c(-10, 5), c(-0.2, 0.2), "default"))
#> Summary of Posterior Distribution 
#> 
#> Parameter   | Median |         95% CI |     pd |           ROPE | % in ROPE
#> ---------------------------------------------------------------------------
#> (Intercept) |  39.04 | [29.35, 48.29] |   100% | [-10.00, 5.00] |        0%
#> wt          |  -5.43 | [-6.71, -4.23] |   100% | [ -0.20, 0.20] |        0%
#> gear        |  -0.30 | [-1.86,  1.56] | 63.00% | [ -0.10, 0.10] |     8.42%
#> 
#> Parameter   |  Rhat |    ESS
#> ----------------------------
#> (Intercept) | 1.002 | 169.00
#> wt          | 1.002 | 217.00
#> gear        | 0.997 | 211.00

# emmeans estimates
# -----------------------------------------------
describe_posterior(emmeans::emtrends(model, ~1, "wt"))
#> Summary of Posterior Distribution
#> 
#> X1      | Median |         95% CI |   pd |          ROPE | % in ROPE
#> --------------------------------------------------------------------
#> overall |  -5.43 | [-6.71, -4.23] | 100% | [-0.10, 0.10] |        0%

# BayesFactor objects
# -----------------------------------------------
bf <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
describe_posterior(bf)
#> Summary of Posterior Distribution
#> 
#> Parameter  | Median |       95% CI |   pd |          ROPE | % in ROPE
#> ---------------------------------------------------------------------
#> Difference |   1.06 | [0.86, 1.27] | 100% | [-0.10, 0.10] |        0%
#> 
#> Parameter  |       BF |              Prior
#> ------------------------------------------
#> Difference | 1.73e+15 | Cauchy (0 +- 0.71)
describe_posterior(bf, centrality = "all", dispersion = TRUE, test = "all")
#> Summary of Posterior Distribution
#> 
#> Parameter  | Median |  MAD | Mean |   SD |  MAP |       95% CI | p (MAP) |   pd
#> -------------------------------------------------------------------------------
#> Difference |   1.06 | 0.10 | 1.06 | 0.10 | 1.05 | [0.87, 1.27] |  < .001 | 100%
#> 
#> Parameter  | p (ROPE) |   ps |          ROPE | % in ROPE | Equivalence (ROPE)
#> -----------------------------------------------------------------------------
#> Difference |   < .001 | 1.00 | [-0.10, 0.10] |        0% |           Rejected
#> 
#> Parameter  |       BF |              Prior
#> ------------------------------------------
#> Difference | 1.73e+15 | Cauchy (0 +- 0.71)
describe_posterior(bf, ci = c(0.80, 0.90))
#> Summary of Posterior Distribution
#> 
#> Parameter  | Median |       80% CI |       90% CI |   pd |          ROPE
#> ------------------------------------------------------------------------
#> Difference |   1.06 | [0.93, 1.19] | [0.89, 1.23] | 100% | [-0.10, 0.10]
#> 
#> Parameter  | % in ROPE |       BF |              Prior
#> ------------------------------------------------------
#> Difference |        0% | 1.73e+15 | Cauchy (0 +- 0.71)
# }