R/bivpot.R

Defines functions ibvpot

Documented in ibvpot

#' Interpret bivariate threshold exceedance models
#'
#' This is an adaptation of the \pkg{evir} package \code{\link[evir]{interpret.gpdbiv}} function.
#' \code{interpret.fbvpot} was adapted to deal with the output of a call to
#' \code{\link[evd]{fbvpot}} from the \pkg{evd} and to handle families other than the logistic distribution.
#' The likelihood derivation comes from expression 2.10 in Smith et al. (1997).
#' @importFrom evd pbvevd
#' @seealso \code{\link[evir]{interpret.gpdbiv}}
#' @author Leo Belzile, adapting original S code by Alexander McNeil
#' @export
#' @references Smith, Tawn and Coles (1997), Markov chain models for threshold exceedances. \emph{Biometrika},
#' \strong{84}(2), 249--268.
#' @param fitted the output of \code{\link[evd]{fbvpot}} or a list. See Details.
#' @param q a vector of quantiles to consider, on the data scale. Must be greater than the thresholds.
#' @param silent boolean; whether to print the interpretation of the result. Default to \code{FALSE}.
#' @return an invisible numeric vector containing marginal, joint and conditional exceedance probabilities.
#' @details The list \code{fitted} must contain
#' \itemize{
#' \item \code{model} a string; see \code{\link[evd]{bvevd}} for options
#' \item \code{param} a named vector containing the parameters of the \code{model}, as well as parameters
#' \code{scale1}, \code{shape1},\code{scale2} and \code{shape2}, corresponding to marginal GPD parameters.
#' \item \code{threshold} a vector of length 2 containing the two thresholds.
#' \item \code{pat} the proportion of observations above the corresponding \code{threshold}
#' }
#'
#' @examples
#' y <- evd::rgpd(1000,1,1,1)
#' x <- y*rmevspec(n=1000,d=2,sigma=cbind(c(0,0.5),c(0.5,0)),model='hr')
#' mod <- evd::fbvpot(x,threshold = c(1,1),model = 'hr',likelihood ='censored')
#' ibvpot(mod, c(20,20))
ibvpot <- function(fitted, q, silent = FALSE) {
    # If input is an object resulting from a call to 'fpot'
    if (inherits(fitted, c("bvpot", "evd"))) {
        fitted$pat <- fitted$nat[1:2]/nrow(fitted$data)
    }
    if (any(is.null(fitted$model), is.null(fitted$pat), is.null(fitted$threshold), is.null(fitted$param))) {
        stop("Invalid argument for \"fitted\". \nPlease provide a list with components \"model\", \"threshold\", \"pat\",\"param\"\n or elsethe output of \"evd::fbvpot\"")
    }
    if (!all(c("shape1", "scale1", "shape2", "scale2") %in% names(fitted$param))) {
        stop("Invalid arguments for \"param\". Missing marginal parameters.")
    }
    if (length(fitted$pat) != 2 || length(fitted$threshold) != 2 || !is.numeric(fitted$threshold) || any(fitted$pat > 1) || any(fitted$pat <
        0)) {
        stop("Invalid input for \"pat\" or \"threshold\" vector.")
    }
    if (!fitted$model %in% c("log", "alog", "hr", "neglog", "aneglog", "bilog", "negbilog", "ct", "amix")) {
        stop("Invalid model. See documentation in the \"evd\" package")
    }
    if (fitted$model %in% c("log", "hr", "neglog")) {
        modelclass <- "A"
    } else if (fitted$model %in% c("alog", "aneglog")) {
        modelclass <- "B"
    } else if (fitted$model %in% c("bilog", "negbilog", "ct", "amix")) {
        modelclass <- "C"
    }
    bivparnames <- switch(modelclass, A = "dep", B = c("dep", "asy1", "asy2"), C = c("alpha", "beta"))
    if (!all(bivparnames %in% names(fitted$param))) {
        stop("Invalid arguments for \"param\". Missing bivariate parameters.")
    }
    # Exponent measure for the postulated model
    if (fitted$model == "ct" && !is.na(fitted$param["rho"])) {
        Vfuncf <- function(q, fitted) {
            rho <- fitted$param["rho"]
            a1 <- fitted$param["alpha"]
            a2 <- fitted$param["beta"]
            Ups <- exp((lbeta(a1, a2 + rho) + log(q[2]))/rho)/(exp((lbeta(a1, a2 + rho) + log(q[2]))/rho) + exp((lbeta(a1 + rho, a2) +
                log(q[1]))/rho))
            exp(pbeta(Ups, a2, a1 + rho, log.p = T) - log(q[1])) + exp(pbeta(1 - Ups, a1, a2 + rho, log.p = T) - log(q[2]))
        }
    } else {
        Vfuncf <- function(q, fitted) {
            f <- switch(fitted$model, log = pbvevd(q = q, model = "log", dep = fitted$param["dep"], mar1 = c(1, 1, 1)), alog = pbvevd(q = q,
                model = "alog", dep = fitted$param["dep"], asy = fitted$param[c("asy1", "asy2")], mar1 = c(1, 1, 1)), hr = pbvevd(q = q,
                model = "hr", dep = fitted$param["dep"], mar1 = c(1, 1, 1)), neglog = pbvevd(q = q, model = "neglog", dep = fitted$param["dep"],
                mar1 = c(1, 1, 1)), aneglog = pbvevd(q = q, model = "aneglog", dep = fitted$param["dep"], asy = fitted$param[c("asy1",
                "asy2")], mar1 = c(1, 1, 1)), bilog = pbvevd(q = q, model = "bilog", alpha = fitted$param["alpha"], beta = fitted$param["beta"],
                mar1 = c(1, 1, 1)), negbilog = pbvevd(q = q, model = "negbilog", alpha = fitted$param["alpha"], beta = fitted$param["beta"],
                mar1 = c(1, 1, 1)), ct = pbvevd(q = q, model = "ct", alpha = fitted$param["alpha"], beta = fitted$param["beta"], mar1 = c(1,
                1, 1)), amix = pbvevd(q = q, model = "amix", alpha = fitted$param["alpha"], beta = fitted$param["beta"], mar1 = c(1,
                1, 1)))
            return(-log(f))
        }
    }
    # Approximate joint CDF of bivariate EV model: F(x,y)
    bivcdf <- function(x, y, u1, lambda1, sigma1, xi1, u2, lambda2, sigma2, xi2, vfunc) {
        Zfunc <- function(y, u, lambda, xi, sigma) {
            (lambda^-1) * exp(log(1 + (xi * pmax((y - u), 0))/sigma)/xi)
        }
        1 - vfunc(c(Zfunc(x, u1, lambda1, xi1, sigma1), Zfunc(y, u2, lambda2, xi2, sigma2)), fitted)
    }
    marg <- function(x, u1, lambda1, sigma1, xi1) {
        1 - lambda1 * exp(-log(1 + (xi1 * (x - u1))/sigma1)/xi1)
    }
    bivsurv <- function(x, y, u1, lambda1, xi1, sigma1, u2, lambda2, xi2, sigma2, marg, newfunc, vfunc) {
        1 - marg(x, u1, lambda1, xi1, sigma1) - marg(y, u2, lambda2, xi2, sigma2) + newfunc(x, y, u1, lambda1, xi1, sigma1, u2, lambda2,
            xi2, sigma2, vfunc)
    }
    if (fitted$threshold[1] > q[1])
        stop("Point below x threshold")
    if (fitted$threshold[2] > q[2])
        stop("Point below y threshold")
    p1 <- 1 - marg(q[1], fitted$threshold[1], fitted$pat[1], fitted$param["scale1"], fitted$param["shape1"])  #u, lambda, scale, shape
    p2 <- 1 - marg(q[2], fitted$threshold[2], fitted$pat[2], fitted$param["scale2"], fitted$param["shape2"])
    p12 <- bivsurv(q[1], q[2], fitted$threshold[1], fitted$pat[1], fitted$param["scale1"], fitted$param["shape1"], fitted$threshold[2],
        fitted$pat[2], fitted$param["scale2"], fitted$param["shape2"], marg, bivcdf, Vfuncf)
    if (!silent) {
        cat("Bivariate POT model:", fitted$model, "\n")
        cat("Thresholds:", fitted$threshold[1], fitted$threshold[2], "\n")
        cat("Extreme levels of interest (x,y):", q[1], q[2], "\n")
        cat("P(X > x) =", format(p1), "\n")
        cat("P(Y > y) =", format(p2), "\n")
        cat("P(X > x, Y > y) =", format(p12), "\n")
        cat("P(X > x) * P(Y > y) =", format(p1 * p2), "\n")
        cat("P(Y > y | X > x) =", format(p12/p1), "\n")
        cat("P(X > x | Y > y) =", format(p12/p2), "\n")
    }
    output <- as.numeric(c(p1, p2, p12, p1 * p2, p12/p1, p12/p2))
    names(output) <- c("p(1)", "p(2)", "p(1,2)", "p(1)p(2)", "p(2|1)", "p(1|2)")
    invisible(output)

}

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mev documentation built on April 26, 2022, 1:07 a.m.