| Hypergeometric {stats} | R Documentation |
Density, distribution function, quantile function and random generation for the hypergeometric distribution.
dhyper(x, m, n, k, log = FALSE) phyper(q, m, n, k, lower.tail = TRUE, log.p = FALSE) qhyper(p, m, n, k, lower.tail = TRUE, log.p = FALSE) rhyper(nn, m, n, k)
x, q |
vector of quantiles representing the number of white balls drawn without replacement from an urn which contains both black and white balls. |
m |
the number of white balls in the urn. |
n |
the number of black balls in the urn. |
k |
the number of balls drawn from the urn. |
p |
probability, it must be between 0 and 1. |
nn |
number of observations. If length(nn) > 1, the length
is taken to be the number required. |
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 hypergeometric distribution is used for sampling without
replacement. The density of this distribution with parameters
m, n and k (named Np, N-Np, and
n, respectively in the reference below) is given by
p(x) = choose(m, x) choose(n, k-x) / choose(m+n, k)
for x = 0, ..., k.
dhyper gives the density,
phyper gives the distribution function,
qhyper gives the quantile function, and
rhyper generates random deviates.
Johnson, N. L., Kotz, S., and Kemp, A. W. (1992) Univariate Discrete Distributions, Second Edition. New York: Wiley.
m <- 10; n <- 7; k <- 8 x <- 0:(k+1) rbind(phyper(x, m, n, k), dhyper(x, m, n, k)) all(phyper(x, m, n, k) == cumsum(dhyper(x, m, n, k)))# FALSE ## but error is very small: signif(phyper(x, m, n, k) - cumsum(dhyper(x, m, n, k)), dig=3)