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Compute indices relevant to describe and characterize the posterior distributions.
describe_posterior(
posteriors,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = c("p_direction", "rope"),
rope_range = "default",
rope_ci = 0.89,
...
)# S3 method for numeric
describe_posterior(
posteriors,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = c("p_direction", "rope"),
rope_range = "default",
rope_ci = 0.89,
bf_prior = NULL,
BF = 1,
...
)
# S3 method for stanreg
describe_posterior(
posteriors,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = c("p_direction", "rope"),
rope_range = "default",
rope_ci = 0.89,
bf_prior = NULL,
diagnostic = c("ESS", "Rhat"),
priors = FALSE,
effects = c("fixed", "random", "all"),
parameters = NULL,
BF = 1,
...
)
# S3 method for stanmvreg
describe_posterior(
posteriors,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = "p_direction",
rope_range = "default",
rope_ci = 0.89,
bf_prior = NULL,
diagnostic = c("ESS", "Rhat"),
priors = FALSE,
effects = c("fixed", "random", "all"),
parameters = NULL,
...
)
# S3 method for MCMCglmm
describe_posterior(
posteriors,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = c("p_direction", "rope"),
rope_range = "default",
rope_ci = 0.89,
diagnostic = "ESS",
parameters = NULL,
...
)
# S3 method for brmsfit
describe_posterior(
posteriors,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = c("p_direction", "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,
BF = 1,
...
)
# S3 method for BFBayesFactor
describe_posterior(
posteriors,
centrality = "median",
dispersion = FALSE,
ci = 0.89,
ci_method = "hdi",
test = c("p_direction", "rope", "bf"),
rope_range = "default",
rope_ci = 0.89,
priors = TRUE,
...
)
A vector, data frame or model of posterior draws.
The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: "median", "mean", "MAP" or "all".
Logical, if TRUE, computes indices of dispersion related to the estimate(s) (SD and MAD for mean and median, respectively).
Value or vector of probability of the CI (between 0 and 1)
to be estimated. Default to .89 (89%) for Bayesian models and .95 (95%) for frequentist models.
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'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).
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.
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.
The amount of support required to be included in the support interval.
Diagnostic metrics to compute. Character (vector) or list with one or more of these options: "ESS", "Rhat", "MCSE" or "all".
Add the prior used for each parameter.
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
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.
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.
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).
# 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))
# }
# NOT RUN {
# 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))
}
# }
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