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dQXGP.R
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dQXGP.R
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#' The Quasi XGamma Poisson distribution
#'
#' @author Simon Zapata
#'
#' @description
#' Density, distribution function,quantile function,
#' random generation and hazard function for the Quasi XGamma Poisson distribution
#' with parameters \code{mu}, \code{sigma} and \code{nu}.
#'
#' @param x,q vector of quantiles.
#' @param p vector of probabilities.
#' @param n number of observations.
#' @param mu parameter.
#' @param sigma parameter.
#' @param nu parameter.
#' @param log,log.p logical; if TRUE, probabilities p are given as log(p).
#' @param lower.tail logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].
#'
#' @details
#' The Quasi XGamma Poisson distribution with parameters \code{mu},
#' \code{sigma} and \code{nu} has density given by:
#'
#' \eqn{f(x)= K(\mu, \sigma, \nu)(\frac {\sigma^{2} x^{2}}{2} + \mu)
#' exp(\frac{\nu exp(-\sigma x)(1 + \mu + \sigma x + \frac {\sigma^{2}x^{2}}{2})}{1+\mu} - \sigma x),}
#'
#' for \eqn{x > 0}, \eqn{\mu> 0}, \eqn{\sigma> 0}, \eqn{\nu> 1}.
#'
#' where
#'
#' \eqn{K(\mu, \sigma, \nu) = \frac{\nu \sigma}{(exp(\nu)-1)(1+\mu)}}
#'
#' @return
#' \code{dQXGP} gives the density, \code{pQXGP} gives the distribution
#' function, \code{qQXGP} gives the quantile function, \code{rQXGP}
#' generates random deviates and \code{hQXGP} gives the hazard function.
#'
#' @example examples/examples_dQXGP.R
#'
#' @references
#' \insertRef{subhradev2018}{RelDists}
#'
#' @importFrom Rdpack reprompt
#'
#' @export
dQXGP <- function(x, mu, sigma, nu, log = FALSE){
if (any(x <= 0))
stop(paste("x must be positive", "\n", ""))
if (any(mu <= 0 ))
stop(paste("mu must be positive", "\n", ""))
if (any(sigma <= 0))
stop(paste("sigma must be positive", "\n", ""))
if (any(nu <= 0))
stop(paste("nu must be positive", "\n", ""))
K <- nu * sigma / ((exp(nu) - 1) * (1 + mu))
A <- (1 + mu + (sigma * x) + (1 / 2 * (sigma^2) * (x^2)))
Term <- nu * exp(-sigma * x) * A / (1+mu)
loglik <- log(K) + log(mu + (1/2 * (sigma^2) * (x^2))) + Term - (sigma * x)
if(log == FALSE)
density <- exp(loglik)
else
density <- loglik
return(density)
}
#' @export
#' @rdname dQXGP
pQXGP <- function(q, mu, sigma, nu, lower.tail=TRUE, log.p=FALSE){
if (any(q <= 0))
stop(paste("x must be positive", "\n", ""))
if (any(mu <= 0 ))
stop(paste("mu must be positive", "\n", ""))
if (any(sigma <= 0))
stop(paste("sigma must be positive", "\n", ""))
if (any(nu <= 0))
stop(paste("nu must be positive", "\n", ""))
Term1 <- nu * exp(-sigma * q)
Term2 <- ((1 + mu + (sigma * q) + ( 1/2 * sigma^2 * q^2)) / (1 + mu))
cdf <- (exp(nu) - exp(Term1 * Term2)) / (exp(nu) - 1)
if (lower.tail == TRUE)
cdf <- cdf
else cdf <- 1 - cdf
if (log.p == FALSE)
cdf <- cdf
else cdf <- log(cdf)
cdf
}
#' @export
#' @rdname dQXGP
qQXGP <- function(p, mu, sigma, nu,
lower.tail=TRUE, log.p=FALSE){
if (any(mu <= 0))
stop(paste("mu must be positive", "\n", ""))
if (any(sigma <= 0))
stop(paste("sigma must be positive", "\n", ""))
if (any(nu <= 0))
stop(paste("nu must be positive", "\n", ""))
if (log.p == TRUE)
p <- exp(p)
else p <- p
if (lower.tail == TRUE)
p <- p
else p <- 1 - p
if (any(p < 0) | any(p > 1))
stop(paste("p must be between 0 and 1", "\n", ""))
fda <- function(x, mu, sigma, nu){
Term1 <- nu * exp(-sigma * x)
Term2 <- ((1 + mu + (sigma * x) + ( 1/2 * sigma^2 * x^2)) / (1 + mu))
cdf <- (exp(nu) - exp(Term1 * Term2)) / (exp(nu) - 1)
}
fda1 <- function(x, mu, sigma, nu, p) {
fda(x, mu, sigma, nu) - p
}
r_de_la_funcion <- function(mu, sigma, nu, p) {
uniroot(fda1, interval=c(0, 1e+06), mu, sigma, nu, p)$root
}
r_de_la_funcion <- Vectorize(r_de_la_funcion)
q <- r_de_la_funcion(mu, sigma, nu, p)
q
}
#' @importFrom stats runif
#' @export
#' @rdname dQXGP
rQXGP <- function(n, mu, sigma, nu){
if(any(n <= 0))
stop(paste("n must be positive","\n",""))
if (any(mu <= 0 ))
stop(paste("mu must be positive", "\n", ""))
if (any(sigma <= 0))
stop(paste("sigma must be positive", "\n", ""))
if (any(nu <= 0))
stop(paste("nu must be positive", "\n", ""))
n <- ceiling(n)
p <- runif(n)
r <- qQXGP(p, mu, sigma, nu)
r
}
#' @export
#' @rdname dQXGP
hQXGP <- function(x, mu, sigma, nu){
if (any(x <= 0))
stop(paste("x must be positive", "\n", ""))
if (any(mu <= 0 ))
stop(paste("mu must be positive", "\n", ""))
if (any(sigma <= 0))
stop(paste("sigma must be positive", "\n", ""))
if (any(nu <= 0))
stop(paste("nu must be positive", "\n", ""))
h <- dQXGP(x, mu, sigma, nu, log=FALSE) /
pQXGP(q=x, mu, sigma, nu, lower.tail=FALSE, log.p=FALSE)
h
}