| Poisson {stats} | R Documentation |
Density, distribution function, quantile function and random
generation for the Poisson distribution with parameter lambda.
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)
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 <= x], otherwise, P[X > x]. |
The Poisson distribution has density
p(x) = lambda^x exp(-lambda)/x!
for x = 0, 1, 2, .... The mean and variance are E(X) = Var(X) = λ.
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 left continuous: qgeom(q, prob) is the largest
integer x such that P(X <= x) < q.
Setting lower.tail = FALSE allows to get much more precise
results when the default, lower.tail = TRUE would return 1, see
the example below.
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.
dpois uses C code contributed by Catherine Loader
(see dbinom).
ppois uses pgamma.
qpois uses the Cornish–Fisher Expansion to include a skewness
correction to a normal approximation, followed by a search.
rpois uses
Ahrens, J. H. and Dieter, U. (1982). Computer generation of Poisson deviates from modified normal distributions. ACM Transactions on Mathematical Software, 8, 163–179.
dbinom for the binomial and dnbinom for
the negative binomial distribution.
-log(dpois(0:7, lambda=1) * gamma(1+ 0:7)) # == 1
Ni <- rpois(50, lam= 4); table(factor(Ni, 0:max(Ni)))
1 - ppois(10*(15:25), lambda=100) # becomes 0 (cancellation)
ppois(10*(15:25), lambda=100, lower=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")