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

#### Documented in cBivariateAMHcdBivariateAMHcrBivariateAMH

#' Bivariate Ali-Mikhail-Haq Copula
#'
#' @description
#' Computes CDF, PDF and simulations of of the bivariate Ali-Mikhail-Haq
#' copula.
#'
#' @details
#'  The bivariate Ali-Mikhail-Haq copula has CDF :
#'
#' @template u1u2-template
#' @template dependencyParameter-template
#' @template dots-template
#'
#' @return
#' Function :
#'   \itemize{
#'     \item \code{\link{cBivariateAMH}}  returns the value of the copula.
#'     \item \code{\link{cdBivariateAMH}}  returns the value of the density copula.
#'     \item \code{\link{crBivariateAMH}}  returns simulated values of the copula.
#'   }
#'
#'
#' @name bivariateAMH
NULL

#' @rdname bivariateAMH
#'
#' @export
#'
#' @examples
#' cBivariateAMH(u1 = .76, u2 = 0.4, dependencyParameter = 0.4)
#'
cBivariateAMH <- function(u1, u2, dependencyParameter, ...) {
stopifnot(
u1 >= 0, u1 <= 1,
u2 >= 0, u2 <= 1,
dependencyParameter >= -1, dependencyParameter <= 1
)

(u1 * u2) / (1 - dependencyParameter * (1 - u1) * (1 - u2))
}

#' @rdname bivariateAMH
#'
#' @export
#'
#' @examples
#' cdBivariateAMH(u1 = .76, u2 = 0.4, dependencyParameter = 0.4)
#'
cdBivariateAMH <- function(u1, u2, dependencyParameter, ...) {
stopifnot(
u1 >= 0, u1 <= 1,
u2 >= 0, u2 <= 1,
dependencyParameter >= -1, dependencyParameter <= 1
)

(
1 - dependencyParameter +
2 * dependencyParameter * cBivariateAMH(u1, u2, dependencyParameter, ...)
) / (
1 - dependencyParameter * (1 - u1) * (1 - u2)
)^2
}

#' @rdname bivariateAMH
#'
#' @template numberSimulations-template
#' @template seed-template
#'
#' @importFrom stats runif qexp
#'
#' @export
#'
#' @examples
#' crBivariateAMH(numberSimulations = 10, seed = 42, dependencyParameter = 0.2)
#'
crBivariateAMH <- function(numberSimulations = 1E4, seed = 42, dependencyParameter) {
stopifnot(
numberSimulations > 0,
dependencyParameter >= -1, dependencyParameter <= 1
)

set.seed(seed)
simulatedUniforms <- matrix(
stats::runif(2 * numberSimulations, 0, 1),
nrow = numberSimulations, ncol = 2
)
simulatedCopula <- matrix(0, nrow = numberSimulations, ncol = 2)

pmfGeometricValues <- c(0, dependencyParameter * (1 - dependencyParameter)^(1:(numberSimulations - 1) - 1))
set.seed(seed * 10)
simulatedGeometrics <- sample(0:(numberSimulations - 1), numberSimulations, replace = TRUE, prob = pmfGeometricValues)

simulatedExponentials <- t(sapply(1:nrow(simulatedUniforms), function(row) stats::qexp(simulatedUniforms[row, 1:2], simulatedGeometrics[row])))
simulatedCopula <- sapply(1:2, function(col) (1 - dependencyParameter) / (exp(simulatedExponentials[, col]) - dependencyParameter))

simulatedCopula
}


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