flexCountReg (version 0.1.1)

PoissonLognormal: Poisson-Lognormal Distribution

Description

These functions provide density, distribution function, quantile function, and random number generation for the Poisson-Lognormal (PLogN) Distribution

Usage

dpLnorm(x, mean = 1, sigma = 1, ndraws = 1500, log = FALSE, hdraws = NULL)
ppLnorm(
  q,
  mean = 1,
  sigma = 1,
  ndraws = 1500,
  lower.tail = TRUE,
  log.p = FALSE
)
qpLnorm(p, mean = 1, sigma = 1, ndraws = 1500)
rpLnorm(n, mean = 1, sigma = 1, ndraws = 1500)

Value

dpLnorm gives the density, ppLnorm gives the distribution function, qpLnorm gives the quantile function, and rpLnorm generates random deviates.

The length of the result is determined by n for rpLnorm, and is the maximum of the lengths of the numerical arguments for the other functions.

Arguments

x

numeric value or a vector of values.

mean

numeric value or vector of mean values for the distribution (the values have to be greater than 0).

sigma

single value or vector of values for the sigma parameter of the lognormal distribution (the values have to be greater than 0).

ndraws

the number of Halton draws to use for the integration.

log

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

hdraws

and optional vector of Halton draws to use for the integration.

q

quantile or a vector of quantiles.

lower.tail

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

log.p

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

p

probability or a vector of probabilities.

n

the number of random numbers to generate.

Details

dpLnorm computes the density (PDF) of the Poisson-Lognormal Distribution.

ppLnorm computes the CDF of the Poisson-Lognormal Distribution.

qpLnorm computes the quantile function of the Poisson-Lognormal Distribution.

rpLnorm generates random numbers from the Poisson-Lognormal Distribution.

The compound Probability Mass Function (PMF) for the Poisson-Lognormal distribution is: $$f(y|\mu,\theta,\alpha)= \int_0^\infty \frac{\mu^y x^y e^{-\mu x}}{y!} \frac{exp\left(-\frac{ln^2(x)}{2\sigma^2} \right)}{x\sigma\sqrt{2\pi}}dx$$

Where \(\sigma\) is a parameter for the lognormal distribution with the restriction \(\sigma>0\), and \(y\) is a non-negative integer.

The expected value of the distribution is: $$E[y]=e^{X\beta+\sigma^2/2} = \mu e^{\sigma^2/2}$$ Halton draws are used to perform simulation over the lognormal distribution to solve the integral.

Examples

dpLnorm(0, mean=0.75, sigma=2, ndraws=10)
ppLnorm(c(0,1,2,3,5,7,9,10), mean=0.75, sigma=2, ndraws=10)
qpLnorm(c(0.1,0.3,0.5,0.9,0.95), mean=0.75, sigma=2, ndraws=10)
rpLnorm(30, mean=0.75,  sigma=2, ndraws=10)