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#' Parameters bootstrapping
#'
#' Compute bootstrapped parameters and their related indices such as Confidence Intervals (CI) and p-values.
#'
#'
#' @param test The indices to compute. Character (vector) with one or more of
#' these options: `"p-value"` (or `"p"`), `"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. [bayestestR::rope()] or
#' [bayestestR::p_direction()]) and its results included in the summary output.
#' @param ... Arguments passed to other methods, like [`bootstrap_model()`] or
#' [`bayestestR::describe_posterior()`].
#' @inheritParams bootstrap_model
#' @inheritParams bayestestR::describe_posterior
#'
#' @return A data frame summarizing the bootstrapped parameters.
#'
#' @inheritSection bootstrap_model Using with **emmeans**
#'
#' @references
#' Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their
#' application (Vol. 1). Cambridge university press.
#'
#' @seealso [`bootstrap_model()`], [`simulate_parameters()`], [`simulate_model()`]
#'
#' @details This function first calls [`bootstrap_model()`] to generate
#' bootstrapped coefficients. The resulting replicated for each coefficient
#' are treated as "distribution", and is passed to [`bayestestR::describe_posterior()`]
#' to calculate the related indices defined in the `"test"` argument.
#'
#' Note that that p-values returned here are estimated under the assumption of
#' *translation equivariance*: that shape of the sampling distribution is
#' unaffected by the null being true or not. If this assumption does not hold,
#' p-values can be biased, and it is suggested to use proper permutation tests
#' to obtain non-parametric p-values.
#'
#' @examplesIf require("boot", quietly = TRUE) && require("emmeans", quietly = TRUE)
#' \donttest{
#' set.seed(2)
#' model <- lm(Sepal.Length ~ Species * Petal.Width, data = iris)
#' b <- bootstrap_parameters(model)
#' print(b)
#'
#' # different type of bootstrapping
#' set.seed(2)
#' b <- bootstrap_parameters(model, type = "balanced")
#' print(b)
#'
#' est <- emmeans::emmeans(b, trt.vs.ctrl ~ Species)
#' print(model_parameters(est))
#' }
#' @export
bootstrap_parameters <- function(model, ...) {
UseMethod("bootstrap_parameters")
}
# methods ----------------------------------------------------------------------
#' @rdname bootstrap_parameters
#' @export
bootstrap_parameters.default <- function(model,
iterations = 1000,
centrality = "median",
ci = 0.95,
ci_method = "quantile",
test = "p-value",
...) {
boot_data <- bootstrap_model(model, iterations = iterations, ...)
bootstrap_parameters(boot_data, centrality = centrality, ci = ci, ci_method = ci_method, test = test, ...)
}
#' @export
bootstrap_parameters.bootstrap_model <- function(model,
centrality = "median",
ci = 0.95,
ci_method = "quantile",
test = "p-value",
...) {
out <- .summary_bootstrap(
data = model,
test = test,
centrality = centrality,
ci = ci,
ci_method = ci_method,
...
)
class(out) <- c("bootstrap_parameters", "parameters_model", class(out))
attr(out, "boot_samples") <- model
out
}
#' @export
model_parameters.bootstrap_model <- bootstrap_parameters.bootstrap_model
# utilities --------------------------------------------------------------------
#' @keywords internal
.summary_bootstrap <- function(data, test, centrality, ci, ci_method, ...) {
# Is the p-value requested?
if (any(test %in% c("p-value", "p", "pval"))) {
p_value <- TRUE
test <- setdiff(test, c("p-value", "p", "pval"))
if (length(test) == 0) test <- NULL
} else {
p_value <- FALSE
}
parameters <- bayestestR::describe_posterior(
data,
centrality = centrality,
ci = ci,
ci_method = ci_method,
test = test,
...
)
# Remove unnecessary columns
if ("CI" %in% names(parameters) && insight::n_unique(parameters$CI) == 1) {
parameters$CI <- NULL
} else if ("CI" %in% names(parameters) && insight::n_unique(parameters$CI) > 1) {
parameters <- datawizard::reshape_ci(parameters)
}
# Coef
if (length(centrality) == 1) {
names(parameters)[names(parameters) == insight::format_capitalize(centrality)] <- "Coefficient"
}
# p-value
if (p_value) {
parameters$.row_order <- seq_len(nrow(parameters))
# calculate probability of direction, then convert to p.
p <- bayestestR::p_direction(data, null = 0, ...)
p$p <- as.numeric(bayestestR::pd_to_p(p$pd))
p$pd <- NULL
parameters <- merge(parameters, p, all = TRUE)
parameters <- parameters[order(parameters$.row_order), ]
parameters$.row_order <- NULL
}
rownames(parameters) <- NULL
attr(parameters, "ci") <- ci
parameters
}
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