bayestestR (version 0.2.0)

describe_posterior: Describe Posterior Distributions

Description

Compute indices relevant to describe and characterise the posterior distributions.

Usage

describe_posterior(posteriors, centrality = "median",
  dispersion = TRUE, ci = 0.89, ci_method = "hdi", test = c("pd",
  "rope"), rope_range = "default", rope_ci = 0.89, ...)
# S3 method for numeric
describe_posterior(posteriors, centrality = "median",
  dispersion = TRUE, ci = 0.89, ci_method = "hdi", test = c("pd",
  "rope"), rope_range = "default", rope_ci = 0.89, bf_prior = NULL,
  ...)
# S3 method for stanreg
describe_posterior(posteriors, centrality = "median",
  dispersion = FALSE, ci = 0.89, ci_method = "hdi", test = c("pd",
  "rope"), rope_range = "default", rope_ci = 0.89, bf_prior = NULL,
  diagnostic = c("ESS", "Rhat"), priors = TRUE, effects = c("fixed",
  "random", "all"), parameters = NULL, ...)
# S3 method for brmsfit
describe_posterior(posteriors, centrality = "median",
  dispersion = FALSE, ci = 0.89, ci_method = "hdi", test = c("pd",
  "rope"), rope_range = "default", rope_ci = 0.89, bf_prior = NULL,
  diagnostic = c("ESS", "Rhat"), effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all"),
  parameters = NULL, ...)
# S3 method for BFBayesFactor
describe_posterior(posteriors,
  centrality = "median", dispersion = FALSE, ci = 0.89,
  ci_method = "hdi", test = c("pd", "rope", "bf"),
  rope_range = "default", rope_ci = 0.89, priors = TRUE, ...)

Arguments

posteriors

A vector, dataframe or model of posterior draws.

centrality

The point-estimates (centrality indices) to compute. Can be a character or a list with "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 interval (between 0 and 1) to be estimated. Named Credible Interval (CI) for consistency.

ci_method

The type of index used for Credible Interval. Can be hdi (default) or "quantile" (see ci).

test

The indices of effect existence to compute. Can be a character or a list with "p_direction", "rope", "p_map" or "bayesfactor".

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.

...

Additional arguments to be passed to or from methods.

bf_prior

Distribution representing a prior for the computation of Bayes factors. Used if the input is a posterior, otherwise (in the case of models) ignored.

diagnostic

Diagnostic metrics to compute. Can be a character or a list with "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.

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.

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.

References

• Vignette In-Depth 1: Comparison of Point-Estimates

• Region of Practical Equivalence (ROPE)

• Bayes Factors

Examples

# NOT RUN {
library(bayestestR)

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))

# rstanarm models
# -----------------------------------------------
library(rstanarm)
model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200)
describe_posterior(model)
describe_posterior(model, centrality = "all", dispersion = TRUE, test = "all")
describe_posterior(model, ci = c(0.80, 0.90))
# }
# NOT RUN {
# brms models
# -----------------------------------------------
library(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
# -----------------------------------------------
library(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))
# }
# NOT RUN {
# }