dQXGP {RelDists}

The Quasi XGamma Poisson distribution

Description

Density, distribution function,quantile function, random generation and hazard function for the Quasi XGamma Poisson distribution with parameters mu, sigma and nu.

Usage

dQXGP(x, mu, sigma, nu, log = FALSE)

pQXGP(q, mu, sigma, nu, lower.tail = TRUE, log.p = FALSE)

qQXGP(p, mu, sigma, nu, lower.tail = TRUE, log.p = FALSE)

rQXGP(n, mu, sigma, nu)

hQXGP(x, mu, sigma, nu)

Arguments

x, q

vector of quantiles.

mu

parameter.

sigma

parameter.

nu

parameter.

log, log.p

logical; if TRUE, probabilities p are given as log(p).

lower.tail

logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

p

vector of probabilities.

n

number of observations.

Details

The Quasi XGamma Poisson distribution with parameters mu, sigma and nu has density given by:

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 x > 0, \mu> 0, \sigma> 0, \nu> 1.

where

K(\mu, \sigma, \nu) = \frac{\nu \sigma}{(exp(\nu)-1)(1+\mu)}

Value

dQXGP gives the density, pQXGP gives the distribution function, qQXGP gives the quantile function, rQXGP generates random deviates and hQXGP gives the hazard function.

Author(s)

Simon Zapata

References

Subhradev S, Mustafa C K, Haitham M Y (2018). “The Quasi XGamma-Poisson distribution: Properties and Application.” Istatistik: Journal of the Turkish Statistical Assocation, 11(3), 65–76. ISSN 1300-4077, https://dergipark.org.tr/en/pub/ijtsa/issue/42850/518206.

Examples

old_par <- par(mfrow = c(1, 1)) # save previous graphical parameters

## The probability density function
curve(dQXGP(x, mu=0.5, sigma=1, nu=1), from=0.1, to=8,
      ylim=c(0, 0.6), col="red", las=1, ylab="f(x)")

## The cumulative distribution and the Reliability function
par(mfrow=c(1, 2))
curve(pQXGP(x, mu=0.5, sigma=1, nu=1),
      from=0.1, to=8,  col="red", las=1, ylab="F(x)")
curve(pQXGP(x,  mu=0.5, sigma=1, nu=1, lower.tail=FALSE),
      from=0.1, to=8, col="red", las=1, ylab="R(x)")

## The quantile function
p <- seq(from=0, to=0.99999, length.out=100)
plot(x=qQXGP(p, mu=0.5, sigma=1, nu=1), y=p, xlab="Quantile",
     las=1, ylab="Probability")
curve(pQXGP(x, mu=0.5, sigma=1, nu=1),
      from=0.1, add=TRUE, col="red")
      
## The random function
hist(rQXGP(n=1000, mu=0.5, sigma=1, nu=1), freq=FALSE,
     xlab="x", ylim=c(0, 0.4), las=1, main="", xlim=c(0, 15))
curve(dQXGP(x, mu=0.5, sigma=1, nu=1),
      from=0.001, to=500, add=TRUE, col="red")

## The Hazard function
curve(hQXGP(x, mu=0.5, sigma=1, nu=1), from=0.01, to=3,
      col="red", ylab="Hazard function", las=1)

par(old_par) # restore previous graphical parameters