#' Parameters from Bayesian Models
#'
#' Parameters from Bayesian models.
#'
#' @param model Bayesian model (including SEM from **blavaan**. May also be
#' a data frame with posterior samples, however, `as_draws` must be set to
#' `TRUE` (else, for data frames `NULL` is returned).
#' @param ci Credible Interval (CI) level. Default to `0.95` (`95%`). See
#' [bayestestR::ci()] for further details.
#' @param group_level Logical, for multilevel models (i.e. models with random
#' effects) and when `effects = "all"` or `effects = "random"`,
#' include the parameters for each group level from random effects. If
#' `group_level = FALSE` (the default), only information on SD and COR
#' are shown.
#' @param component Which type of parameters to return, such as parameters for the
#' conditional model, the zero-inflation part of the model, the dispersion
#' term, or other auxiliary parameters be returned? Applies to models with
#' zero-inflation and/or dispersion formula, or if parameters such as `sigma`
#' should be included. May be abbreviated. Note that the *conditional*
#' component is also called *count* or *mean* component, depending on the
#' model. There are three convenient shortcuts: `component = "all"` returns
#' all possible parameters. If `component = "location"`, location parameters
#' such as `conditional`, `zero_inflated`, or `smooth_terms`, 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`, or `beta`
#' (and other auxiliary parameters) are returned.
#' @param as_draws Logical, if `TRUE` and `model` is of class `data.frame`,
#' the data frame is treated as posterior samples and handled similar to
#' Bayesian models. All arguments in `...` are passed to
#' `model_parameters.draws()`.
#' @inheritParams model_parameters.default
#' @inheritParams bayestestR::describe_posterior
#' @inheritParams insight::get_parameters
#'
#' @seealso [insight::standardize_names()] to
#' rename columns into a consistent, standardized naming scheme.
#'
#' @note When `standardize = "refit"`, columns `diagnostic`,
#' `bf_prior` and `priors` refer to the *original*
#' `model`. If `model` is a data frame, arguments `diagnostic`,
#' `bf_prior` and `priors` are ignored. \cr \cr There is also a
#' [`plot()`-method](https://easystats.github.io/see/articles/parameters.html)
#' implemented in the
#' [**see**-package](https://easystats.github.io/see/).
#'
#' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
#'
#' @examples
#' \donttest{
#' library(parameters)
#' if (require("rstanarm")) {
#' model <- suppressWarnings(stan_glm(
#' Sepal.Length ~ Petal.Length * Species,
#' data = iris, iter = 500, refresh = 0
#' ))
#' model_parameters(model)
#' }
#' }
#' @return A data frame of indices related to the model's parameters.
#' @export
model_parameters.stanreg <- function(model,
centrality = "median",
dispersion = FALSE,
ci = 0.95,
ci_method = "eti",
test = "pd",
rope_range = "default",
rope_ci = 0.95,
bf_prior = NULL,
diagnostic = c("ESS", "Rhat"),
priors = TRUE,
effects = "fixed",
exponentiate = FALSE,
standardize = NULL,
group_level = FALSE,
keep = NULL,
drop = NULL,
verbose = TRUE,
...) {
# Processing
params <- .extract_parameters_bayesian(
model,
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
bf_prior = bf_prior,
diagnostic = diagnostic,
priors = priors,
effects = effects,
standardize = standardize,
keep_parameters = keep,
drop_parameters = drop,
verbose = verbose,
...
)
if (effects != "fixed") {
random_effect_levels <- which(
params$Effects %in% "random" & !startsWith(params$Parameter, "Sigma[")
)
if (length(random_effect_levels) && isFALSE(group_level)) {
params <- params[-random_effect_levels, , drop = FALSE]
}
}
## TODO: can we use the regular pretty-name-formatting?
params <- .add_pretty_names(params, model)
# exponentiate coefficients and SE/CI, if requested
params <- .exponentiate_parameters(params, model, exponentiate)
params <- .add_model_parameters_attributes(
params,
model,
ci,
exponentiate,
ci_method = ci_method,
group_level = group_level,
verbose = verbose,
...
)
attr(params, "parameter_info") <- insight::clean_parameters(model)
attr(params, "object_name") <- insight::safe_deparse_symbol(substitute(model))
class(params) <- c("parameters_model", "see_parameters_model", class(params))
params
}
#' @export
model_parameters.stanmvreg <- function(model,
centrality = "median",
dispersion = FALSE,
ci = 0.95,
ci_method = "eti",
test = "pd",
rope_range = "default",
rope_ci = 0.95,
bf_prior = NULL,
diagnostic = c("ESS", "Rhat"),
priors = TRUE,
effects = "fixed",
standardize = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...) {
# Processing
params <- .extract_parameters_bayesian(
model,
centrality = centrality,
dispersion = dispersion,
ci = ci,
ci_method = ci_method,
test = test,
rope_range = rope_range,
rope_ci = rope_ci,
bf_prior = bf_prior,
diagnostic = diagnostic,
priors = priors,
effects = effects,
standardize = standardize,
keep_parameters = keep,
drop_parameters = drop,
verbose = verbose,
...
)
params$Parameter <- gsub("^(.*)\\|(.*)", "\\2", params$Parameter)
params <- .add_pretty_names(params, model)
attr(params, "ci") <- ci
attr(params, "object_name") <- insight::safe_deparse_symbol(substitute(model))
class(params) <- c("parameters_model", "see_parameters_model", class(params))
params
}
#' @export
standard_error.stanreg <- standard_error.brmsfit
#' @export
standard_error.mvstanreg <- standard_error.brmsfit
#' @export
p_value.stanreg <- p_value.BFBayesFactor
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