# R/CompBinom.R In Distributacalcul: Probability Distribution Functions

#### Documented in expValCompBinompCompBinomTVatRCompBinomvarCompBinomVatRCompBinom

#' Compound Binomial Distribution
#'
#' @description
#' Computes various risk measures (mean, variance, Value-at-Risk (VaR),
#' and Tail Value-at-Risk (TVaR)) for the compound Binomial distribution.
#'
#' @details
#' The compound binomial distribution has density ....
#'
#' @param x vector of quantiles
#' @template size-prob-template
#' @template distr_severity-template
#' @template k0-template
#' @template shape-template
#' @template rate-template
#' @template scale-template
#' @template vark-template
#'
#' @return
#' Function :
#'   \itemize{
#'     \item \code{\link{pCompBinom}}  gives the cumulative density function.
#'     \item \code{\link{expValCompBinom}}  gives the expected value.
#'     \item \code{\link{varCompBinom}}  gives the variance.
#'     \item \code{\link{TVatRCompBinom}}  gives the Tail Value-at-Risk.
#'     \item \code{\link{VatRCompBinom}}  gives the Value-at-Risk.
#'   }
#' Returned values are approximations for the cumulative density function,
#' TVaR, and VaR.
#'
#' @name CompBinom
NULL

#' @rdname CompBinom
#'
#' @importFrom stats dbinom pgamma
#' @export
#'
#' @examples
#' pCompBinom(x = 2, size = 1, prob = 0.2, shape = log(1000) - 0.405,
#'           rate = 0.9^2, k0 = 1E2, distr_severity = "Gamma")
#'
pCompBinom <- function(x, size, prob, shape, rate = 1 / scale, scale = 1 / rate, k0, distr_severity = "Gamma") {
stopifnot(
prob >= 0, prob <= 1,
size > 0,
size %% 1 == 0,
rate > 0,
k0 >= 0
)
stopifnot(grepl(pattern = "^Gamma$", x = distr_severity, ignore.case = TRUE)) if(grepl(pattern = "^Gamma$", x = distr_severity, ignore.case = TRUE)) {
stopifnot(shape > 0)
stats::dbinom(x = 0, size = size, prob = prob) + sum(
stats::dbinom(x = 1:k0, size = size, prob = prob) *
stats::pgamma(q = x, shape = shape * 1:k0, rate = rate)
)
}
}

#' @rdname CompBinom
#'
#' @export
#'
#' @examples
#' expValCompBinom(size = 1, prob = 0.2, shape = log(1000) - 0.405, rate = 0.9^2,
#'           distr_severity = "Lognormale")
#'
expValCompBinom <- function(size, prob, shape, rate = 1 / scale, scale = 1 / rate, distr_severity = "Gamma") {
stopifnot(
prob >= 0, prob <= 1,
size > 0,
size %% 1 == 0,
rate > 0
)

if (grepl(pattern = "^Gamma$", x = distr_severity, ignore.case = TRUE)) { stopifnot(shape > 0) expValGamma(shape, rate) * expValBinom(size, prob) } else if (grepl(pattern = "^Lognormal[e]*$", x = distr_severity, ignore.case = TRUE)) {
expValLnorm(shape, sqrt(rate)) * expValBinom(size, prob)
}
}

#' @rdname CompBinom
#'
#' @export
#'
#' @examples
#' varCompBinom(size = 1, prob = 0.2, shape = log(1000) - 0.405, rate = 0.9^2,
#'           distr_severity = "Lognormale")
#'
varCompBinom <- function(size, prob, shape, rate = 1 / scale, scale = 1 / rate, distr_severity = "Gamma") {
stopifnot(
prob >= 0, prob <= 1,
size > 0,
size %% 1 == 0,
rate > 0
)

if (grepl(pattern = "^Gamma$", x = distr_severity, ignore.case = TRUE)) { stopifnot(shape > 0) (shape / rate)^2 * size * prob * (1 - prob) + size * prob * varGamma(shape, rate) } else if (grepl(pattern = "^Lognormal[e]*$", x = distr_severity, ignore.case = TRUE)) {
expValLnorm(shape, sqrt(rate))^2 * size * prob * (1 - prob) + size * prob * varLnorm(shape, sqrt(rate))
}
}

#' @rdname CompBinom
#'
#' @template kap-template
#'
#' @export
#'
#' @examples
#' VatRCompBinom(kap = 0.9, size = 1, prob = 0.2, shape = log(1000) - 0.405,
#'             rate = 0.9^2, k0 = 1E2, distr_severity = "Gamma")
#'
VatRCompBinom <- function(kap, size, prob, shape, rate = 1 / scale, scale = 1 / rate, k0, distr_severity = "Gamma") {
stopifnot(
kap >= 0, kap <= 1,
prob >= 0, prob <= 1,
size > 0,
size %% 1 == 0,
rate > 0
)
stopifnot(grepl(pattern = "(^Gamma$)", x = distr_severity, ignore.case = TRUE)) if (kap <= pCompBinom(x = 0, size, prob, shape, rate, k0 = k0, distr_severity = distr_severity)) { VaR.CompBinom <- 0 } else { stopifnot(shape > 0) VaR.CompBinom <- stats::optimize(function(i) abs(pCompBinom(x = i, size, prob, shape, rate, k0 = k0, distr_severity = distr_severity) - kap), c(0, k0))$minimum
}

return(VaR.CompBinom)
}

#' @rdname CompBinom
#'
#' @importFrom stats dbinom pgamma
#' @export
#'
#' @examples
#' vark_calc <- VatRCompBinom(kap = 0.9, size = 1, prob = 0.2, shape = 0.59,
#'             rate = 0.9^2, k0 = 1E2, distr_severity = "Gamma")
#' TVatRCompBinom(kap = 0.9, size = 1, prob = 0.2, shape = 0.59, rate = 0.9^2,
#'             vark = vark_calc, k0 = 1E2, distr_severity = "Gamma")
#'
TVatRCompBinom <- function(kap, size, prob, shape, rate = 1 / scale, scale = 1 / rate, vark, k0, distr_severity = "Gamma") {
stopifnot(
kap >= 0, kap <= 1,
prob >= 0, prob <= 1,
size > 0,
size %% 1 == 0,
rate > 0,
vark >= 0
)

if (vark == 0) {
TVaR.BNCOMP <- expValCompBinom(size, prob, shape, rate, distr_severity = distr_severity) / (1 - kap)
} else if (grepl(pattern = "^Gamma\$", x = distr_severity, ignore.case = TRUE)) {
stopifnot(shape > 0)
TVaR.BNCOMP <- sum(
stats::dbinom(x = 1:k0, size = size, prob = prob) *
expValGamma(shape, rate) * 1:k0 *
stats::pgamma(q = vark, shape = shape * 1:k0 + 1, rate = rate, lower.tail = FALSE)
) / (1 - kap)
}

return(TVaR.BNCOMP)
}


## Try the Distributacalcul package in your browser

Any scripts or data that you put into this service are public.

Distributacalcul documentation built on May 29, 2024, 9:25 a.m.