Generalised Poisson distribution

Probability mass function, distribution function, and random generation for the generalised Poisson distribution.

Usage

dgenpois(x, lambda = 1, phi = 1, log = FALSE)

pgenpois(q, lambda = 1, phi = 1, lower.tail = TRUE, log.p = FALSE)

qgenpois(p, lambda = 1, phi = 1,
         lower.tail = TRUE, log.p = FALSE, max.value = 10000)

rgenpois(n, lambda = 1, phi = 1, max.value = 10000)

Arguments

x, q

integer vector of counts

lambda

vector of positive means

phi

vector of non-negative dispersion parameters

log, log.p

logical; return log-density if TRUE

lower.tail

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

p

vector of probabilities

max.value

a constant, set to the default value of 10000 for how far the algorithm should look for q.

n

number of random values to return.

Value

dgenpois gives the probability mass function, pgenpois gives the distribution function, qgenpois gives the quantile function, and rgenpois generates random deviates.

Details

This implementation of dgenpois allows for automatic differentiation with RTMB. The other functions are imported from gamlss.dist::GPO.

The distribution has mean \(\lambda\) and variance \(\lambda(1 + \phi \lambda)^2\). For \(\phi = 0\) it reduces to the Poisson distribution, however \(\phi\) must be strictly positive here.

Examples

set.seed(123)
x <- rgenpois(1, 2, 3)
d <- dgenpois(x, 2, 3)
p <- pgenpois(x, 2, 3)
q <- qgenpois(p, 2, 3)