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#' Interpret Bayesian Diagnostic Indices
#'
#' Interpretation of Bayesian diagnostic indices, such as Effective Sample Size (ESS) and Rhat.
#'
#' @param ess Value or vector of Effective Sample Size (ESS) values.
#' @param rhat Value or vector of Rhat values.
#' @param rules A character string (see *Rules*) or a custom set of [rules()].
#'
#' @section Rules:
#'
#' ## ESS
#' - Bürkner, P. C. (2017) (`"burkner2017"`; default)
#' - **ESS < 1000** - Insufficient
#' - **ESS >= 1000** - Sufficient
#'
#' ## Rhat
#' - Vehtari et al. (2019) (`"vehtari2019"`; default)
#' - **Rhat < 1.01** - Converged
#' - **Rhat >= 1.01** - Failed
#' - Gelman & Rubin (1992) (`"gelman1992"`)
#' - **Rhat < 1.1** - Converged
#' - **Rhat >= 1.1** - Failed
#'
#'
#' @examples
#' interpret_ess(1001)
#' interpret_ess(c(852, 1200))
#'
#' interpret_rhat(1.00)
#' interpret_rhat(c(1.5, 0.9))
#' @references
#' - Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models
#' using Stan. Journal of Statistical Software, 80(1), 1-28.
#'
#' - Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation
#' using multiple sequences. Statistical science, 7(4), 457-472.
#'
#' - Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P. C.
#' (2019). Rank-normalization, folding, and localization: An improved Rhat for
#' assessing convergence of MCMC. arXiv preprint arXiv:1903.08008.
#'
#' @keywords interpreters
#' @export
interpret_ess <- function(ess, rules = "burkner2017") {
rules <- .match.rules(
rules,
list(
burkner2017 = rules(1000, c("insufficient", "sufficient"), name = "burkner2017", right = FALSE)
)
)
interpret(ess, rules)
}
#' @rdname interpret_ess
#' @export
interpret_rhat <- function(rhat, rules = "vehtari2019") {
rules <- .match.rules(
rules,
list(
vehtari2019 = rules(1.01, c("converged", "failed"), name = "vehtari2019"),
gelman1992 = rules(1.1, c("converged", "failed"), name = "gelman1992")
)
)
interpret(rhat, rules)
}
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