dhpois {distributions3} R Documentation

## The hurdle Poisson distribution

### Description

Density, distribution function, quantile function, and random generation for the zero-hurdle Poisson distribution with parameters lambda and pi.

### Usage

dhpois(x, lambda, pi, log = FALSE)

phpois(q, lambda, pi, lower.tail = TRUE, log.p = FALSE)

qhpois(p, lambda, pi, lower.tail = TRUE, log.p = FALSE)

rhpois(n, lambda, pi)


### Arguments

 x vector of (non-negative integer) quantiles. lambda vector of (non-negative) Poisson parameters. pi vector of zero-hurdle probabilities in the unit interval. log, log.p logical indicating whether probabilities p are given as log(p). q vector of quantiles. lower.tail logical indicating whether probabilities are P[X \le x] (lower tail) or P[X > x] (upper tail). p vector of probabilities. n number of random values to return.

### Details

All functions follow the usual conventions of d/p/q/r functions in base R. In particular, all four hpois functions for the hurdle Poisson distribution call the corresponding pois functions for the Poisson distribution from base R internally.

Note, however, that the precision of qhpois for very large probabilities (close to 1) is limited because the probabilities are internally handled in levels and not in logs (even if log.p = TRUE).

HurdlePoisson, dpois

### Examples

## theoretical probabilities for a hurdle Poisson distribution
x <- 0:8
p <- dhpois(x, lambda = 2.5, pi = 0.75)
plot(x, p, type = "h", lwd = 2)

## corresponding empirical frequencies from a simulated sample
set.seed(0)
y <- rhpois(500, lambda = 2.5, pi = 0.75)
hist(y, breaks = -1:max(y) + 0.5)



[Package distributions3 version 0.2.1 Index]