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# classes: .averaging
#################### .averaging
#' Parameters from special models
#'
#' Parameters from special regression models not listed under one of the previous categories yet.
#'
#' @param component Model component for which parameters should be shown. May be
#' one of `"conditional"`, `"precision"` (**betareg**),
#' `"scale"` (**ordinal**), `"extra"` (**glmx**),
#' `"marginal"` (**mfx**), `"conditional"` or `"full"` (for
#' `MuMIn::model.avg()`) or `"all"`.
#' @param include_studies Logical, if `TRUE` (default), includes parameters
#' for all studies. Else, only parameters for overall-effects are shown.
#' @inheritParams model_parameters.default
#' @inheritParams model_parameters.stanreg
#' @inheritParams simulate_model
#'
#' @seealso [insight::standardize_names()] to rename
#' columns into a consistent, standardized naming scheme.
#'
#' @examples
#' library(parameters)
#' if (require("brglm2", quietly = TRUE)) {
#' data("stemcell")
#' model <- bracl(
#' research ~ as.numeric(religion) + gender,
#' weights = frequency,
#' data = stemcell,
#' type = "ML"
#' )
#' model_parameters(model)
#' }
#' @return A data frame of indices related to the model's parameters.
#' @export
model_parameters.averaging <- function(model,
ci = 0.95,
component = c("conditional", "full"),
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...) {
component <- match.arg(component)
out <- .model_parameters_generic(
model = model,
ci = ci,
merge_by = "Parameter",
exponentiate = exponentiate,
component = component,
p_adjust = p_adjust,
keep_parameters = keep,
drop_parameters = drop,
summary = summary,
...
)
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(model))
out
}
#' @export
standard_error.averaging <- function(model, component = "conditional", ...) {
component <- match.arg(component, choices = c("conditional", "full"))
params <- insight::get_parameters(model, component = component)
if (component == "full") {
s <- summary(model)$coefmat.full
} else {
s <- summary(model)$coefmat.subset
}
.data_frame(
Parameter = .remove_backticks_from_string(params$Parameter),
SE = as.vector(s[, 3])
)
}
#' @rdname p_value.DirichletRegModel
#' @export
p_value.averaging <- function(model, component = c("conditional", "full"), ...) {
component <- match.arg(component)
params <- insight::get_parameters(model, component = component)
if (component == "full") {
s <- summary(model)$coefmat.full
} else {
s <- summary(model)$coefmat.subset
}
.data_frame(
Parameter = .remove_backticks_from_string(params$Parameter),
p = as.vector(s[, 5])
)
}
#' @export
ci.averaging <- function(x, ci = 0.95, component = c("conditional", "full"), ...) {
component <- match.arg(component)
.ci_generic(model = x, ci = ci, dof = Inf, component = component)
}
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