stats (version 3.6.2)

# Poisson: The Poisson Distribution

## Description

Density, distribution function, quantile function and random generation for the Poisson distribution with parameter lambda.

## Usage

dpois(x, lambda, log = FALSE)
ppois(q, lambda, lower.tail = TRUE, log.p = FALSE)
qpois(p, lambda, lower.tail = TRUE, log.p = FALSE)
rpois(n, lambda)

## Arguments

x

vector of (non-negative integer) quantiles.

q

vector of quantiles.

p

vector of probabilities.

n

number of random values to return.

lambda

vector of (non-negative) means.

log, log.p

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

lower.tail

logical; if TRUE (default), probabilities are $$P[X \le x]$$, otherwise, $$P[X > x]$$.

## Value

dpois gives the (log) density, ppois gives the (log) distribution function, qpois gives the quantile function, and rpois generates random deviates.

Invalid lambda will result in return value NaN, with a warning.

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

The numerical arguments other than n are recycled to the length of the result. Only the first elements of the logical arguments are used.

## Details

The Poisson distribution has density $$p(x) = \frac{\lambda^x e^{-\lambda}}{x!}$$ for $$x = 0, 1, 2, \ldots$$ . The mean and variance are $$E(X) = Var(X) = \lambda$$.

Note that $$\lambda = 0$$ is really a limit case (setting $$0^0 = 1$$) resulting in a point mass at $$0$$, see also the example.

If an element of x is not integer, the result of dpois is zero, with a warning. $$p(x)$$ is computed using Loader's algorithm, see the reference in dbinom.

The quantile is right continuous: qpois(p, lambda) is the smallest integer $$x$$ such that $$P(X \le x) \ge p$$.

Setting lower.tail = FALSE allows to get much more precise results when the default, lower.tail = TRUE would return 1, see the example below.

Distributions for other standard distributions, including dbinom for the binomial and dnbinom for the negative binomial distribution.

poisson.test.

## Examples

# NOT RUN {
require(graphics)

-log(dpois(0:7, lambda = 1) * gamma(1+ 0:7)) # == 1
Ni <- rpois(50, lambda = 4); table(factor(Ni, 0:max(Ni)))

1 - ppois(10*(15:25), lambda = 100)  # becomes 0 (cancellation)
ppois(10*(15:25), lambda = 100, lower.tail = FALSE)  # no cancellation

par(mfrow = c(2, 1))
x <- seq(-0.01, 5, 0.01)
plot(x, ppois(x, 1), type = "s", ylab = "F(x)", main = "Poisson(1) CDF")
plot(x, pbinom(x, 100, 0.01), type = "s", ylab = "F(x)",
main = "Binomial(100, 0.01) CDF")

## The (limit) case  lambda = 0 :
stopifnot(identical(dpois(0,0), 1),
identical(ppois(0,0), 1),
identical(qpois(1,0), 0))
# }