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describe_posterior.R
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describe_posterior.R
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#' Describe Posterior Distributions
#'
#' Compute indices relevant to describe and characterize the posterior distributions.
#'
#' @param 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.
#' @param ci_method The type of index used for Credible Interval. Can be
#' `"ETI"` (default, see [bayestestR::eti()]), `"HDI"`
#' (see [bayestestR::hdi()]), `"BCI"` (see
#' [bayestestR::bci()]), `"SPI"` (see [bayestestR::spi()]), or
#' `"SI"` (see [bayestestR::si()]).
#' @param 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 \pkg{bayestestR} function is called
#' (e.g. [bayestestR::rope()] or [bayestestR::p_direction()]) and its results
#' included in the summary output.
#' @param 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)`.
#' @param rope_ci The Credible Interval (CI) probability, corresponding to the
#' proportion of HDI, to use for the percentage in ROPE.
#' @param 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 [bayestestR::reshape_iterations()].
#' @param 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.
#' @param priors Add the prior used for each parameter.
#'
#' @inheritParams point_estimate
#' @inheritParams ci
#' @inheritParams si
#' @inheritParams hdi
#'
#' @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).
#'
#' @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)](https://easystats.github.io/bayestestR/articles/region_of_practical_equivalence.html)
#' - [Bayes factors](https://easystats.github.io/bayestestR/articles/bayes_factors.html)
#'
#' @examples
#' 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)
#' ))
#' }
#' \donttest{
#' # 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))
#' describe_posterior(model, rope_range = list(c(-10, 5), c(-0.2, 0.2), "default"))
#'
#' # 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))
#' }
#' }
#' @export
describe_posterior <- function(posterior, ...) {
UseMethod("describe_posterior")
}
#' @export
describe_posterior.default <- function(posterior, ...) {
insight::format_error(
paste0("`describe_posterior()` is not yet implemented for objects of class `", class(posterior)[1], "`.")
)
}
#' @keywords internal
.describe_posterior <- function(x,
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,
...) {
if (is.null(x)) {
if (verbose) {
insight::format_warning("Could not extract posterior samples.")
}
return(NULL)
}
# we need this information from the original object
if (all(rope_range == "default")) {
rope_range <- rope_range(x, verbose = verbose, ...)
}
if (!is.data.frame(x) && !is.numeric(x)) {
is_stanmvreg <- inherits(x, "stanmvreg")
cleaned_parameters <- insight::clean_parameters(x)
# rename to use `x` in bayes factor later
x_df <- insight::get_parameters(x, ...)
} else {
cleaned_parameters <- NULL
x_df <- x
}
# Arguments fixes
if (!is.null(centrality) && length(centrality) == 1 && (centrality == "none" || isFALSE(centrality))) {
centrality <- NULL
}
if (!is.null(ci) && length(ci) == 1 && (is.na(ci) || isFALSE(ci))) {
ci <- NULL
}
if (!is.null(test) && length(test) == 1 && (test == "none" || isFALSE(test))) {
test <- NULL
}
# Point-estimates
if (is.null(centrality)) {
estimates <- data.frame(Parameter = NA)
} else {
estimates <- .prepare_output(
point_estimate(x_df, centrality = centrality, dispersion = dispersion, ...),
cleaned_parameters,
is_stanmvreg
)
if (!"Parameter" %in% names(estimates)) {
estimates <- cbind(
data.frame(Parameter = "Posterior", stringsAsFactors = FALSE),
estimates
)
}
}
# Uncertainty
if (is.null(ci)) {
uncertainty <- data.frame(Parameter = NA)
} else {
ci_method <- match.arg(tolower(ci_method), c("hdi", "spi", "quantile", "ci", "eti", "si", "bci", "bcai"))
# not sure why "si" requires the model object
if (ci_method == "si") {
uncertainty <- ci(x, BF = BF, method = ci_method, prior = bf_prior, verbose = verbose, ...)
} else {
uncertainty <- ci(x_df, ci = ci, method = ci_method, verbose = verbose, ...)
}
uncertainty <- .prepare_output(
uncertainty,
cleaned_parameters,
is_stanmvreg
)
if (!"Parameter" %in% names(uncertainty)) {
uncertainty <- cbind(
data.frame(Parameter = "Posterior", stringsAsFactors = FALSE),
uncertainty
)
}
}
# Effect Existence
if (is.null(test)) {
test_pd <- data.frame(
Parameter = NA,
Effects = NA,
Component = NA,
Response = NA
)
test_rope <- data.frame(
Parameter = NA,
Effects = NA,
Component = NA,
Response = NA
)
test_prope <- data.frame(
Parameter = NA,
Effects = NA,
Component = NA,
Response = NA
)
test_psig <- data.frame(
Parameter = NA,
Effects = NA,
Component = NA,
Response = NA
)
test_bf <- data.frame(
Parameter = NA,
Effects = NA,
Component = NA,
Response = NA
)
test_pmap <- data.frame(
Parameter = NA,
Effects = NA,
Component = NA,
Response = NA
)
} else {
test <- .check_test_values(test)
if ("all" %in% test) {
test <- c("pd", "p_map", "p_rope", "p_significance", "rope", "equivalence", "bf")
}
## TODO no BF for arm::sim
if (inherits(x, c("sim", "sim.merMod", "mcmc", "stanfit"))) {
test <- setdiff(test, "bf")
}
## TODO enable once "rope()" works for multi-response models
# no ROPE for multi-response models
if (insight::is_multivariate(x)) {
test <- setdiff(test, c("rope", "p_rope"))
if (verbose) {
insight::format_warning(
"Multivariate response models are not yet supported for tests `rope` and `p_rope`."
)
}
}
# MAP-based p-value
if (any(c("p_map", "p_pointnull") %in% test)) {
test_pmap <- .prepare_output(
p_map(x_df, ...),
cleaned_parameters,
is_stanmvreg
)
if (!is.data.frame(test_pmap)) {
test_pmap <- data.frame(
Parameter = "Posterior",
p_MAP = test_pmap,
stringsAsFactors = FALSE
)
}
} else {
test_pmap <- data.frame(Parameter = NA)
}
# Probability of direction
if (any(c("pd", "p_direction", "pdir", "mpe") %in% test)) {
test_pd <- .prepare_output(
p_direction(x_df, ...),
cleaned_parameters,
is_stanmvreg
)
if (!is.data.frame(test_pd)) {
test_pd <- data.frame(
Parameter = "Posterior",
pd = test_pd,
stringsAsFactors = FALSE
)
}
} else {
test_pd <- data.frame(Parameter = NA)
}
# Probability of rope
if ("p_rope" %in% test) {
test_prope <- .prepare_output(
p_rope(x_df, range = rope_range, verbose = verbose, ...),
cleaned_parameters,
is_stanmvreg
)
if (!"Parameter" %in% names(test_prope)) {
test_prope <- cbind(
data.frame(Parameter = "Posterior", stringsAsFactors = FALSE),
test_prope
)
}
} else {
test_prope <- data.frame(Parameter = NA)
}
# Probability of significance
if (any(c("ps", "p_sig", "p_significance") %in% test)) {
test_psig <- .prepare_output(
p_significance(x_df, threshold = rope_range, ...),
cleaned_parameters,
is_stanmvreg
)
if (!is.data.frame(test_psig)) {
test_psig <- data.frame(
Parameter = "Posterior",
ps = test_psig,
stringsAsFactors = FALSE
)
}
} else {
test_psig <- data.frame(Parameter = NA)
}
# ROPE
if ("rope" %in% test) {
test_rope <- .prepare_output(
rope(x_df, range = rope_range, ci = rope_ci, ...),
cleaned_parameters,
is_stanmvreg
)
if (!"Parameter" %in% names(test_rope)) {
test_rope <- cbind(
data.frame(Parameter = "Posterior", stringsAsFactors = FALSE),
test_rope
)
}
names(test_rope)[names(test_rope) == "CI"] <- "ROPE_CI"
} else {
test_rope <- data.frame(Parameter = NA)
}
# Equivalence test
if (any(c("equivalence", "equivalence_test", "equitest") %in% test)) {
dot_args <- list(...)
dot_args$verbose <- !"rope" %in% test
test_equi <- .prepare_output(
do.call(
equivalence_test,
c(
dot_args,
list(
x = x_df,
range = rope_range,
ci = rope_ci
)
)
),
cleaned_parameters,
is_stanmvreg
)
test_equi$Cleaned_Parameter <- NULL
if (!"Parameter" %in% names(test_equi)) {
test_equi <- cbind(
data.frame(Parameter = "Posterior", stringsAsFactors = FALSE),
test_equi
)
}
names(test_equi)[names(test_equi) == "CI"] <- "ROPE_CI"
test_rope <- merge(test_rope, test_equi, all = TRUE)
test_rope <- test_rope[!names(test_rope) %in% c("HDI_low", "HDI_high")]
}
# Bayes Factors
if (any(c("bf", "bayesfactor", "bayes_factor") %in% test)) {
test_bf <- tryCatch(
.prepare_output(
bayesfactor_parameters(x, prior = bf_prior, verbose = verbose, ...),
cleaned_parameters,
is_stanmvreg
),
error = function(e) data.frame(Parameter = NA)
)
if (!"Parameter" %in% names(test_bf)) {
test_bf <- cbind(
data.frame(Parameter = "Posterior", stringsAsFactors = FALSE),
test_bf
)
}
} else {
test_bf <- data.frame(Parameter = NA)
}
}
# for data frames or numeric, and even for some models, we don't
# have the "Effects" or "Component" column for all data frames.
# To make "merge()" work, we add those columns to all data frames,
# filled with NA, and remove the columns later if necessary
estimates <- .add_effects_component_column(estimates)
uncertainty <- .add_effects_component_column(uncertainty)
test_pmap <- .add_effects_component_column(test_pmap)
test_pd <- .add_effects_component_column(test_pd)
test_prope <- .add_effects_component_column(test_prope)
test_psig <- .add_effects_component_column(test_psig)
test_rope <- .add_effects_component_column(test_rope)
test_bf <- .add_effects_component_column(test_bf)
# at least one "valid" data frame needs a row id, to restore
# row-order after merging
if (!all(is.na(estimates$Parameter))) {
estimates$.rowid <- seq_len(nrow(estimates))
} else if (!all(is.na(test_pmap$Parameter))) {
test_pmap$.rowid <- seq_len(nrow(test_pmap))
} else if (!all(is.na(test_pd$Parameter))) {
test_pd$.rowid <- seq_len(nrow(test_pd))
} else if (!all(is.na(test_prope$Parameter))) {
test_prope$.rowid <- seq_len(nrow(test_prope))
} else if (!all(is.na(test_psig$Parameter))) {
test_psig$.rowid <- seq_len(nrow(test_psig))
} else if (!all(is.na(test_rope$Parameter))) {
test_rope$.rowid <- seq_len(nrow(test_rope))
} else if (!all(is.na(test_bf$Parameter))) { # nolint
test_bf$.rowid <- seq_len(nrow(test_bf))
} else {
estimates$.rowid <- seq_len(nrow(estimates))
}
# remove duplicated columns
if (all(c("rope", "p_rope") %in% test)) {
test_prope$ROPE_low <- NULL
test_prope$ROPE_high <- NULL
}
# merge all data frames
merge_by <- c("Parameter", "Effects", "Component", "Response")
# merge_by <- intersect(merge_by, colnames(estimates))
out <- merge(estimates, uncertainty, by = merge_by, all = TRUE)
out <- merge(out, test_pmap, by = merge_by, all = TRUE)
out <- merge(out, test_pd, by = merge_by, all = TRUE)
out <- merge(out, test_prope, by = merge_by, all = TRUE)
out <- merge(out, test_psig, by = merge_by, all = TRUE)
out <- merge(out, test_rope, by = merge_by, all = TRUE)
out <- merge(out, test_bf, by = merge_by, all = TRUE)
out <- out[!is.na(out$Parameter), ]
# check which columns can be removed at the end. In any case, we don't
# need .rowid in the returned data frame, and when the Effects or Component
# column consist only of missing values, we remove those columns as well
remove_columns <- ".rowid"
if (insight::n_unique(out$Effects, remove_na = TRUE) < 2) remove_columns <- c(remove_columns, "Effects")
if (insight::n_unique(out$Component, remove_na = TRUE) < 2) remove_columns <- c(remove_columns, "Component")
if (insight::n_unique(out$Response, remove_na = TRUE) < 2) remove_columns <- c(remove_columns, "Response")
# Restore columns order
out <- datawizard::data_remove(out[order(out$.rowid), ], remove_columns, verbose = FALSE)
# Add iterations
if (keep_iterations) {
row_order <- out$Parameter
iter <- as.data.frame(t(as.data.frame(x_df, ...)))
names(iter) <- paste0("iter_", seq_len(ncol(iter)))
iter$Parameter <- row.names(iter)
out <- merge(out, iter, all.x = TRUE, by = "Parameter")
out <- out[match(row_order, out$Parameter), ]
row.names(out) <- NULL
}
# Prepare output
attr(out, "ci_method") <- ci_method
out
}
#' @keywords internal
.add_effects_component_column <- function(x) {
if (!"Effects" %in% names(x)) x <- cbind(x, data.frame(Effects = NA))
if (!"Component" %in% names(x)) x <- cbind(x, data.frame(Component = NA))
if (!"Response" %in% names(x)) x <- cbind(x, data.frame(Response = NA))
x
}
# Models based on simple data frame of posterior ---------------------
#' @rdname describe_posterior
#' @export
describe_posterior.numeric <- function(posterior,
centrality = "median",
dispersion = FALSE,
ci = 0.95,
ci_method = "eti",
test = c("p_direction", "rope"),
rope_range = "default",
rope_ci = 0.95,
keep_iterations = FALSE,
bf_prior = NULL,
BF = 1,
verbose = TRUE,
...) {
out <- .describe_posterior(
posterior,
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
keep_iterations = keep_iterations,
bf_prior = bf_prior,
BF = BF,
verbose = verbose,
...
)
class(out) <- unique(c("describe_posterior", "see_describe_posterior", class(out)))
out
}
#' @export
describe_posterior.double <- describe_posterior.numeric
#' @export
#' @rdname describe_posterior
#' @inheritParams p_direction
describe_posterior.data.frame <- function(posterior,
centrality = "median",
dispersion = FALSE,
ci = 0.95,
ci_method = "eti",
test = c("p_direction", "rope"),
rope_range = "default",
rope_ci = 0.95,
keep_iterations = FALSE,
bf_prior = NULL,
BF = 1,
rvar_col = NULL,
verbose = TRUE,
...) {
x_rvar <- .possibly_extract_rvar_col(posterior, rvar_col)
if (length(x_rvar) > 0L) {
cl <- match.call()
cl[[1]] <- bayestestR::describe_posterior
cl$posterior <- x_rvar
cl$rvar_col <- NULL
prior_rvar <- .possibly_extract_rvar_col(posterior, bf_prior)
if (length(prior_rvar) > 0L) {
cl$bf_prior <- prior_rvar
}
out <- eval.parent(cl)
obj_name <- insight::safe_deparse_symbol(substitute(posterior))
attr(out, "object_name") <- sprintf('%s[["%s"]]', obj_name, rvar_col)
return(.append_datagrid(out, posterior))
}
out <- .describe_posterior(
posterior,
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
keep_iterations = keep_iterations,
bf_prior = bf_prior,
BF = BF,
verbose = verbose,
...
)
class(out) <- unique(c("describe_posterior", "see_describe_posterior", class(out)))
out
}
#' @export
describe_posterior.sim.merMod <- describe_posterior.numeric
#' @export
describe_posterior.sim <- describe_posterior.numeric
#' @export
describe_posterior.bayesQR <- function(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,
parameters = NULL,
verbose = TRUE,
...) {
out <- .describe_posterior(
insight::get_parameters(posterior),
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
keep_iterations = keep_iterations,
effects = "fixed",
parameters = parameters,
verbose = verbose,
...
)
attr(out, "ci_method") <- ci_method
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(posterior))
class(out) <- c("describe_posterior", "see_describe_posterior", class(out))
out
}
#' @export
describe_posterior.blrm <- describe_posterior.bayesQR
#' @export
describe_posterior.mcmc <- describe_posterior.bayesQR
#' @export
describe_posterior.mcmc.list <- describe_posterior.bayesQR
#' @export
describe_posterior.BGGM <- describe_posterior.bayesQR
#' @export
describe_posterior.draws <- function(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,
...) {
out <- .describe_posterior(
.posterior_draws_to_df(posterior),
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
keep_iterations = keep_iterations,
bf_prior = if (!is.null(bf_prior)) .posterior_draws_to_df(bf_prior),
BF = BF,
verbose = verbose,
...
)
class(out) <- unique(c("describe_posterior", "see_describe_posterior", class(out)))
out
}
#' @export
describe_posterior.rvar <- describe_posterior.draws
# easystats methods ------------------------
#' @export
describe_posterior.effectsize_std_params <- function(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,
...) {
class(posterior) <- "data.frame"
no_unique <- vapply(posterior, function(col) {
length(unique(col)) == 1
}, FUN.VALUE = TRUE)
if (any(no_unique)) {
no_unique <- which(no_unique)
out <- describe_posterior.data.frame(
posterior[, -no_unique],
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
keep_iterations = keep_iterations,
bf_prior = bf_prior,
BF = BF,
verbose = verbose,
...
)
out_int <- data.frame(Parameter = colnames(posterior)[no_unique])
col_diff <- setdiff(colnames(out), colnames(out_int))
out_int[, col_diff] <- NA
out <- rbind(out_int, out)
out <- out[order(match(out$Parameter, colnames(posterior))), ]
return(out)
}
describe_posterior.data.frame(
posterior,
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
keep_iterations = keep_iterations,
bf_prior = bf_prior,
BF = BF,
verbose = verbose,
...
)
}
#' @export
describe_posterior.get_predicted <- function(posterior,
centrality = "median",
dispersion = FALSE,
ci = 0.95,
ci_method = "eti",
test = NULL,
verbose = TRUE,
...) {
if ("iterations" %in% names(attributes(posterior))) {
describe_posterior(
as.data.frame(t(attributes(posterior)$iterations)),
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
verbose = verbose,
...
)
} else {
insight::format_error("No iterations present in the output.")
}
}
# emmeans ---------------------------
#' @export
describe_posterior.emmGrid <- function(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,
...) {
if (any(c("all", "bf", "bayesfactor", "bayes_factor") %in% tolower(test)) ||
"si" %in% tolower(ci_method)) {
samps <- .clean_priors_and_posteriors(posterior, bf_prior, verbose = verbose)
bf_prior <- samps$prior
posterior_samples <- samps$posterior
} else {
posterior_samples <- insight::get_parameters(posterior)
}
out <- .describe_posterior(
posterior_samples,
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
keep_iterations = keep_iterations,
bf_prior = bf_prior,
BF = BF,
verbose = verbose,
...
)
row.names(out) <- NULL # Reset row names
out <- .append_datagrid(out, posterior)
class(out) <- c("describe_posterior", "see_describe_posterior", class(out))
attr(out, "ci_method") <- ci_method
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(posterior))
out
}
#' @export
describe_posterior.emm_list <- describe_posterior.emmGrid
#' @export
describe_posterior.slopes <- function(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,
...) {
if (any(c("all", "bf", "bayesfactor", "bayes_factor") %in% tolower(test)) ||
"si" %in% tolower(ci_method)) {
samps <- .clean_priors_and_posteriors(posterior, bf_prior, verbose = verbose)
bf_prior <- samps$prior
posterior_samples <- samps$posterior
} else {
posterior_samples <- .get_marginaleffects_draws(posterior)
}
out <- describe_posterior(
posterior_samples,
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
keep_iterations = keep_iterations,
bf_prior = bf_prior,
BF = BF,
verbose = verbose,
...
)
row.names(out) <- NULL # Reset row names
out <- .append_datagrid(out, posterior)
class(out) <- c("describe_posterior", "see_describe_posterior", class(out))
attr(out, "ci_method") <- ci_method
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(posterior))
out
}
#' @export
describe_posterior.comparisons <- describe_posterior.slopes
#' @export
describe_posterior.predictions <- describe_posterior.slopes
# Stan ------------------------------
#' @inheritParams insight::get_parameters
#' @inheritParams diagnostic_posterior
#' @rdname describe_posterior
#' @export
describe_posterior.stanreg <- function(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,
...) {
if ((any(c("all", "bf", "bayesfactor", "bayes_factor") %in% tolower(test)) ||
"si" %in% tolower(ci_method)) && is.null(bf_prior)) {
bf_prior <- suppressMessages(unupdate(posterior))
}
effects <- match.arg(effects)
component <- match.arg(component)
out <- .describe_posterior(
posterior,
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
keep_iterations = keep_iterations,
bf_prior = bf_prior,
BF = BF,
effects = effects,
component = component,
parameters = parameters,
verbose = verbose,
...
)
diagnostic <- diagnostic_posterior(
posterior,
diagnostic,
effects = effects,
component = component,