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#' @export
model_parameters.stanfit <- function(model,
centrality = "median",
dispersion = FALSE,
ci = 0.95,
ci_method = "eti",
test = "pd",
rope_range = "default",
rope_ci = 0.95,
diagnostic = c("ESS", "Rhat"),
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 = NULL,
diagnostic = diagnostic,
priors = FALSE,
effects = effects,
standardize = standardize,
keep_parameters = keep,
drop_parameters = drop,
verbose = verbose,
...
)
if (effects != "fixed") {
random_effect_levels <- which(
params$Effects == "random" & !startsWith(params$Parameter, "Sigma[")
)
if (length(random_effect_levels) && isFALSE(group_level)) {
params <- params[-random_effect_levels, ]
}
}
# 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,
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
}
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