#' Confidence/Credible/Compatibility Interval (CI)
#'
#' Compute Confidence/Credible/Compatibility Intervals (CI) or Support Intervals
#' (SI) for Bayesian and frequentist models. The Documentation is accessible
#' for:
#'
#' - [Bayesian models](https://easystats.github.io/bayestestR/articles/credible_interval.html)
#' - [Frequentist models](https://easystats.github.io/parameters/reference/ci.default.html)
#'
#' @param x A `stanreg` or `brmsfit` model, or a vector representing a posterior
#' distribution.
#' @param method Can be ["ETI"][eti] (default), ["HDI"][hdi], ["BCI"][bci],
#' ["SPI"][spi] or ["SI"][si].
#' @param ci Value or vector of probability of the CI (between 0 and 1)
#' to be estimated. Default to `0.95` (`95%`).
#' @inheritParams hdi
#' @inheritParams si
#' @inherit hdi seealso
#' @family ci
#'
#' @return A data frame with following columns:
#'
#' - `Parameter` The model parameter(s), if `x` is a model-object. If `x` is a
#' vector, this column is missing.
#' - `CI` The probability of the credible interval.
#' - `CI_low`, `CI_high` The lower and upper credible interval limits for the parameters.
#'
#' @note When it comes to interpretation, we recommend thinking of the CI in terms of
#' an "uncertainty" or "compatibility" interval, the latter being defined as
#' "Given any value in the interval and the background assumptions,
#' the data should not seem very surprising" (_Gelman & Greenland 2019_).
#'
#' There is also a [`plot()`-method](https://easystats.github.io/see/articles/bayestestR.html) implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}.
#'
#' @references
#' Gelman A, Greenland S. Are confidence intervals better termed "uncertainty
#' intervals"? BMJ 2019;l5381. 10.1136/bmj.l5381
#'
#' @examplesIf require("rstanarm", quietly = TRUE)
#' library(bayestestR)
#'
#' posterior <- rnorm(1000)
#' ci(posterior, method = "ETI")
#' ci(posterior, method = "HDI")
#'
#' df <- data.frame(replicate(4, rnorm(100)))
#' ci(df, method = "ETI", ci = c(0.80, 0.89, 0.95))
#' ci(df, method = "HDI", ci = c(0.80, 0.89, 0.95))
#'
#' model <- suppressWarnings(
#' stan_glm(mpg ~ wt, data = mtcars, chains = 2, iter = 200, refresh = 0)
#' )
#' ci(model, method = "ETI", ci = c(0.80, 0.89))
#' ci(model, method = "HDI", ci = c(0.80, 0.89))
#'
#' @examplesIf require("BayesFactor", quietly = TRUE)
#' bf <- ttestBF(x = rnorm(100, 1, 1))
#' ci(bf, method = "ETI")
#' ci(bf, method = "HDI")
#'
#' @examplesIf require("emmeans", quietly = TRUE) && require("rstanarm", quietly = TRUE)
#' model <- emtrends(model, ~1, "wt", data = mtcars)
#' ci(model, method = "ETI")
#' ci(model, method = "HDI")
#' @export
ci <- function(x, ...) {
UseMethod("ci")
}
#' @keywords internal
.ci_bayesian <- function(x,
ci = 0.95,
method = "ETI",
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
verbose = TRUE,
BF = 1,
...) {
if (tolower(method) %in% c("eti", "equal", "ci", "quantile")) {
return(
eti(
x,
ci = ci,
effects = effects,
component = component,
parameters = parameters,
verbose = verbose,
...
)
)
} else if (tolower(method) %in% c("bci", "bca", "bcai")) {
return(
bci(
x,
ci = ci,
effects = effects,
component = component,
parameters = parameters,
verbose = verbose,
...
)
)
} else if (tolower(method) == "hdi") {
return(
hdi(
x,
ci = ci,
effects = effects,
component = component,
parameters = parameters,
verbose = verbose,
...
)
)
} else if (tolower(method) == "spi") {
return(
spi(
x,
ci = ci,
effects = effects,
component = component,
parameters = parameters,
verbose = verbose,
...
)
)
} else if (tolower(method) == "si") {
return(
si(
x,
BF = BF,
effects = effects,
component = component,
parameters = parameters,
verbose = verbose,
...
)
)
} else {
insight::format_error(paste0(
"`method` should be 'ETI' (for equal-tailed interval), ",
"'HDI' (for highest density interval), 'BCI' (for bias corrected and ",
"accelerated bootstrap intervals), 'SPI' (for shortest probability ",
"interval) or 'SI' (for support interval)."
))
}
}
#' @rdname ci
#' @export
ci.numeric <- function(x, ci = 0.95, method = "ETI", verbose = TRUE, BF = 1, ...) {
.ci_bayesian(x, ci = ci, method = method, verbose = verbose, BF = BF, ...)
}
#' @rdname ci
#' @inheritParams p_direction
#' @export
ci.data.frame <- function(x, ci = 0.95, method = "ETI", BF = 1, rvar_col = NULL, verbose = TRUE, ...) {
x_rvar <- .possibly_extract_rvar_col(x, rvar_col)
if (length(x_rvar) > 0L) {
cl <- match.call()
cl[[1]] <- bayestestR::ci
cl$x <- x_rvar
cl$rvar_col <- NULL
out <- eval.parent(cl)
obj_name <- insight::safe_deparse_symbol(substitute(x))
attr(out, "object_name") <- sprintf('%s[["%s"]]', obj_name, rvar_col)
return(.append_datagrid(out, x, long = length(ci) > 1L))
}
.ci_bayesian(x, ci = ci, method = method, verbose = verbose, BF = BF, ...)
}
#' @export
ci.draws <- function(x, ci = 0.95, method = "ETI", verbose = TRUE, BF = 1, ...) {
.ci_bayesian(.posterior_draws_to_df(x), ci = ci, method = method, verbose = verbose, BF = BF, ...)
}
#' @export
ci.rvar <- ci.draws
#' @export
ci.emmGrid <- function(x, ci = NULL, ...) {
if (!.is_baysian_grid(x)) {
insight::check_if_installed("parameters")
if (is.null(ci)) ci <- 0.95
return(parameters::ci(model = x, ci = ci, ...))
}
if (is.null(ci)) ci <- 0.95
xdf <- insight::get_parameters(x)
out <- ci(xdf, ci = ci, ...)
out <- .append_datagrid(out, x, long = length(ci) > 1L)
out
}
#' @export
ci.emm_list <- ci.emmGrid
#' @export
ci.slopes <- function(x, ci = NULL, ...) {
if (!.is_baysian_grid(x)) {
insight::check_if_installed("parameters")
if (is.null(ci)) ci <- 0.95
return(parameters::ci(model = x, ci = ci, ...))
}
if (is.null(ci)) ci <- 0.95
xrvar <- .get_marginaleffects_draws(x)
out <- ci(xrvar, ci = ci, ...)
out <- .append_datagrid(out, x, long = length(ci) > 1L)
out
}
#' @export
ci.comparisons <- ci.slopes
#' @export
ci.predictions <- ci.slopes
#' @rdname ci
#' @export
ci.sim.merMod <- function(x,
ci = 0.95,
method = "ETI",
effects = c("fixed", "random", "all"),
parameters = NULL,
verbose = TRUE,
...) {
.ci_bayesian(
x,
ci = ci,
method = method,
effects = effects,
parameters = parameters,
verbose = verbose,
...
)
}
#' @rdname ci
#' @export
ci.sim <- function(x,
ci = 0.95,
method = "ETI",
parameters = NULL,
verbose = TRUE,
...) {
.ci_bayesian(
x,
ci = ci,
method = method,
parameters = parameters,
verbose = verbose,
...
)
}
#' @rdname ci
#' @export
ci.stanreg <- function(x,
ci = 0.95,
method = "ETI",
effects = c("fixed", "random", "all"),
component = c(
"location",
"all",
"conditional",
"smooth_terms",
"sigma",
"distributional",
"auxiliary"
),
parameters = NULL,
verbose = TRUE,
BF = 1,
...) {
.ci_bayesian(
x,
ci = ci,
method = method,
effects = effects,
component = component,
parameters = parameters,
verbose = verbose,
BF = BF,
...
)
}
#' @rdname ci
#' @export
ci.brmsfit <- function(x,
ci = 0.95,
method = "ETI",
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
verbose = TRUE,
BF = 1,
...) {
.ci_bayesian(
x,
ci = ci,
method = method,
effects = effects,
component = component,
parameters = parameters,
verbose = verbose,
BF = BF,
...
)
}
#' @export
ci.stanfit <- ci.stanreg
#' @export
ci.blavaan <- ci.stanreg
#' @rdname ci
#' @export
ci.BFBayesFactor <- ci.numeric
#' @rdname ci
#' @export
ci.MCMCglmm <- function(x, ci = 0.95, method = "ETI", verbose = TRUE, ...) {
nF <- x$Fixed$nfl
ci(
as.data.frame(x$Sol[, 1:nF, drop = FALSE]),
ci = ci,
method = method,
verbose = verbose,
...
)
}
#' @export
ci.bamlss <- function(x,
ci = 0.95,
method = "ETI",
component = c("all", "conditional", "location"),
verbose = TRUE,
...) {
component <- match.arg(component)
ci(
insight::get_parameters(x, component = component),
ci = ci,
method = method,
verbose = verbose,
...
)
}
#' @export
ci.bcplm <- function(x, ci = 0.95, method = "ETI", verbose = TRUE, ...) {
ci(insight::get_parameters(x), ci = ci, method = method, verbose = verbose, ...)
}
#' @export
ci.blrm <- ci.bcplm
#' @export
ci.mcmc <- ci.bcplm
#' @export
ci.mcmc.list <- ci.bcplm
#' @export
ci.BGGM <- ci.bcplm
#' @export
ci.get_predicted <- ci.data.frame
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