View source: R/describe_posterior.R
describe_posterior  R Documentation 
Compute indices relevant to describe and characterize the posterior distributions.
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,
...
)
posterior 
A vector, data frame or model of posterior draws.
bayestestR supports a wide range of models (see 
... 
Additional arguments to be passed to or from methods. 
centrality 
The pointestimates (centrality indices) to compute. Character
(vector) or list with one or more of these options: 
dispersion 
Logical, if 
ci 
Value or vector of probability of the CI (between 0 and 1)
to be estimated. Default to 
ci_method 
The type of index used for Credible Interval. Can be

test 
The indices of effect existence to compute. Character (vector) or
list with one or more of these options: 
rope_range 
ROPE's lower and higher bounds. Should be a list of two
values (e.g., 
rope_ci 
The Credible Interval (CI) probability, corresponding to the proportion of HDI, to use for the percentage in ROPE. 
keep_iterations 
If 
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 
diagnostic 
Diagnostic metrics to compute. Character (vector) or list
with one or more of these options: 
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 zeroinflated part of the model be returned? May be abbreviated. Only applies to brmsmodels. 
parameters 
Regular expression pattern that describes the parameters
that should be returned. Metaparameters (like 
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).
Makowski, D., BenShachar, 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")}
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))
# 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))
}
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