https://github.com/cran/bayestestR
Revision d8462ad2168ad7ee61c0d7e679174e775f01a9be authored by Dominique Makowski on 18 January 2020, 07:10 UTC, committed by cran-robot on 18 January 2020, 07:10 UTC
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Tip revision: d8462ad2168ad7ee61c0d7e679174e775f01a9be authored by Dominique Makowski on 18 January 2020, 07:10 UTC
version 0.5.0
Tip revision: d8462ad
eti.R
#' Equal-Tailed Interval (ETI)
#'
#' Compute the \strong{Equal-Tailed Interval (ETI)} of posterior distributions using the quantiles method. The probability of being below this interval is equal to the probability of being above it. The ETI can be used in the context of uncertainty characterisation of posterior distributions as \strong{Credible Interval (CI)}.
#'
#' @inheritParams hdi
#' @inherit ci return
#' @inherit hdi details
#'
#' @examples
#' library(bayestestR)
#'
#' posterior <- rnorm(1000)
#' eti(posterior)
#' eti(posterior, ci = c(.80, .89, .95))
#'
#' df <- data.frame(replicate(4, rnorm(100)))
#' eti(df)
#' eti(df, ci = c(.80, .89, .95))
#'
#' library(rstanarm)
#' model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
#' eti(model)
#' eti(model, ci = c(.80, .89, .95))
#'
#' library(emmeans)
#' eti(emtrends(model, ~1, "wt"))
#' \dontrun{
#' library(brms)
#' model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
#' eti(model)
#' eti(model, ci = c(.80, .89, .95))
#'
#' library(BayesFactor)
#' bf <- ttestBF(x = rnorm(100, 1, 1))
#' eti(bf)
#' eti(bf, ci = c(.80, .89, .95))
#' }
#'
#' @export
eti <- function(x, ...) {
  UseMethod("eti")
}



#' @rdname eti
#' @export
eti.numeric <- function(x, ci = .89, verbose = TRUE, ...) {
  out <- do.call(rbind, lapply(ci, function(i) {
    .eti(x = x, ci = i, verbose = verbose)
  }))
  class(out) <- unique(c("bayestestR_eti", "see_eti", "bayestestR_ci", "see_ci", class(out)))
  attr(out, "data") <- x
  out
}



#' @rdname eti
#' @export
eti.data.frame <- function(x, ci = .89, verbose = TRUE, ...) {
  dat <- .compute_interval_dataframe(x = x, ci = ci, verbose = verbose, fun = "eti")
  attr(dat, "object_name") <- .safe_deparse(substitute(x))
  dat
}



#' @rdname eti
#' @export
eti.MCMCglmm <- function(x, ci = .89, verbose = TRUE, ...) {
  nF <- x$Fixed$nfl
  d <- as.data.frame(x$Sol[, 1:nF, drop = FALSE])
  dat <- .compute_interval_dataframe(x = d, ci = ci, verbose = verbose, fun = "eti")
  attr(dat, "data") <- .safe_deparse(substitute(x))
  dat
}



#' @export
eti.mcmc <- function(x, ci = .89, verbose = TRUE, ...) {
  d <- as.data.frame(x)
  dat <- .compute_interval_dataframe(x = d, ci = ci, verbose = verbose, fun = "eti")
  attr(dat, "data") <- .safe_deparse(substitute(x))
  dat
}



#' @rdname eti
#' @export
eti.sim.merMod <- function(x, ci = .89, effects = c("fixed", "random", "all"), parameters = NULL, verbose = TRUE, ...) {
  effects <- match.arg(effects)
  dat <- .compute_interval_simMerMod(x = x, ci = ci, effects = effects, parameters = parameters, verbose = verbose, fun = "eti")
  out <- dat$result
  attr(out, "data") <- dat$data
  out
}



#' @rdname eti
#' @export
eti.sim <- function(x, ci = .89, parameters = NULL, verbose = TRUE, ...) {
  dat <- .compute_interval_sim(x = x, ci = ci, parameters = parameters, verbose = verbose, fun = "eti")
  out <- dat$result
  attr(out, "data") <- dat$data
  out
}



#' @rdname eti
#' @export
eti.emmGrid <- function(x, ci = .89, verbose = TRUE, ...) {
  if (!requireNamespace("emmeans")) {
    stop("Package 'emmeans' required for this function to work. Please install it by running `install.packages('emmeans')`.")
  }
  xdf <- as.data.frame(as.matrix(emmeans::as.mcmc.emmGrid(x, names = FALSE)))

  dat <- .compute_interval_dataframe(x = xdf, ci = ci, verbose = verbose, fun = "eti")
  attr(dat, "object_name") <- .safe_deparse(substitute(x))
  dat
}


#' @rdname eti
#' @export
eti.stanreg <- function(x, ci = .89, effects = c("fixed", "random", "all"),
                        parameters = NULL, verbose = TRUE, ...) {
  effects <- match.arg(effects)

  out <- .prepare_output(
    eti(insight::get_parameters(x, effects = effects, parameters = parameters), ci = ci, verbose = verbose, ...),
    insight::clean_parameters(x)
  )

  class(out) <- unique(c("bayestestR_eti", "see_eti", class(out)))
  attr(out, "object_name") <- .safe_deparse(substitute(x))
  out
}


#' @rdname eti
#' @export
eti.brmsfit <- function(x, ci = .89, effects = c("fixed", "random", "all"),
                        component = c("conditional", "zi", "zero_inflated", "all"),
                        parameters = NULL, verbose = TRUE, ...) {
  effects <- match.arg(effects)
  component <- match.arg(component)

  out <- .prepare_output(
    eti(insight::get_parameters(x, effects = effects, component = component, parameters = parameters), ci = ci, verbose = verbose, ...),
    insight::clean_parameters(x)
  )

  class(out) <- unique(c("bayestestR_eti", "see_eti", class(out)))
  attr(out, "object_name") <- .safe_deparse(substitute(x))
  out
}


#' @rdname eti
#' @export
eti.BFBayesFactor <- function(x, ci = .89, verbose = TRUE, ...) {
  out <- eti(insight::get_parameters(x), ci = ci, verbose = verbose, ...)
  attr(out, "object_name") <- .safe_deparse(substitute(x))
  out
}


#' @importFrom stats quantile
.eti <- function(x, ci, verbose = TRUE) {
  check_ci <- .check_ci_argument(x, ci, verbose)

  if (!is.null(check_ci)) {
    return(check_ci)
  }

  results <- as.vector(stats::quantile(
    x,
    probs = c((1 - ci) / 2, (1 + ci) / 2),
    names = FALSE
  ))

  data.frame(
    "CI" = ci * 100,
    "CI_low" = results[1],
    "CI_high" = results[2]
  )
}
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