stats (version 3.3.2)

Binomial: The Binomial Distribution

Description

Density, distribution function, quantile function and random generation for the binomial distribution with parameters size and prob. This is conventionally interpreted as the number of ‘successes’ in size trials.

Usage

dbinom(x, size, prob, log = FALSE)
pbinom(q, size, prob, lower.tail = TRUE, log.p = FALSE)
qbinom(p, size, prob, lower.tail = TRUE, log.p = FALSE)
rbinom(n, size, prob)

Arguments

x, q

vector of quantiles.

p

vector of probabilities.

n

number of observations. If length(n) > 1, the length is taken to be the number required.

size

number of trials (zero or more).

prob

probability of success on each trial.

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

dbinom gives the density, pbinom gives the distribution function, qbinom gives the quantile function and rbinom generates random deviates. If size is not an integer, NaN is returned. The length of the result is determined by n for rbinom, 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 binomial distribution with size \(= n\) and prob \(= p\) has density $$p(x) = {n \choose x} {p}^{x} {(1-p)}^{n-x}$$ for \(x = 0, \ldots, n\). Note that binomial coefficients can be computed by choose in R. If an element of x is not integer, the result of dbinom is zero, with a warning. \(p(x)\) is computed using Loader's algorithm, see the reference below. The quantile is defined as the smallest value \(x\) such that \(F(x) \ge p\), where \(F\) is the distribution function.

See Also

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

Examples

require(graphics)
# Compute P(45 < X < 55) for X Binomial(100,0.5)
sum(dbinom(46:54, 100, 0.5))

## Using "log = TRUE" for an extended range :
n <- 2000
k <- seq(0, n, by = 20)
plot (k, dbinom(k, n, pi/10, log = TRUE), type = "l", ylab = "log density",
      main = "dbinom(*, log=TRUE) is better than  log(dbinom(*))")
lines(k, log(dbinom(k, n, pi/10)), col = "red", lwd = 2)
## extreme points are omitted since dbinom gives 0.
mtext("dbinom(k, log=TRUE)", adj = 0)
mtext("extended range", adj = 0, line = -1, font = 4)
mtext("log(dbinom(k))", col = "red", adj = 1)