\HeaderA{Binomial}{The Binomial Distribution}{Binomial}
\aliasA{dbinom}{Binomial}{dbinom}
\aliasA{pbinom}{Binomial}{pbinom}
\aliasA{qbinom}{Binomial}{qbinom}
\aliasA{rbinom}{Binomial}{rbinom}
\keyword{distribution}{Binomial}
\begin{Description}\relax
Density, distribution function, quantile function and random
generation for the binomial distribution with parameters \code{size}
and \code{prob}.
\end{Description}
\begin{Usage}
\begin{verbatim}
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)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x, q}] vector of quantiles.
\item[\code{p}] vector of probabilities.
\item[\code{n}] number of observations. If \code{length(n) > 1}, the length
is taken to be the number required.
\item[\code{size}] number of trials.
\item[\code{prob}] probability of success on each trial.
\item[\code{log, log.p}] logical; if TRUE, probabilities p are given as log(p).
\item[\code{lower.tail}] logical; if TRUE (default), probabilities are
\eqn{P[X \le x]}{P[X <= x]}, otherwise, \eqn{P[X > x]}{P[X > x]}.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The binomial distribution with \code{size} \eqn{= n}{} and
\code{prob} \eqn{= p}{} has density
\deqn{p(x) = {n \choose x} {p}^{x} {(1-p)}^{n-x}}{p(x) = choose(n,x) p^x (1-p)^(n-x)}
for \eqn{x = 0, \ldots, n}{}.

If an element of \code{x} is not integer, the result of \code{dbinom}
is zero, with a warning.
\eqn{p(x)}{} is computed using Loader's algorithm, see the reference below.

The quantile is defined as the smallest value \eqn{x}{} such that
\eqn{F(x) \ge p}{}, where \eqn{F}{} is the distribution function.
\end{Details}
\begin{Value}
\code{dbinom} gives the density, \code{pbinom} gives the distribution
function, \code{qbinom} gives the quantile function and \code{rbinom}
generates random deviates.

If \code{size} is not an integer, \code{NaN} is returned.
\end{Value}
\begin{References}\relax
Catherine Loader (2000). \emph{Fast and Accurate Computation of
Binomial Probabilities}; manuscript available from
\url{http://cm.bell-labs.com/cm/ms/departments/sia/catherine/dbinom}
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{dnbinom}{dnbinom}} for the negative binomial, and
\code{\LinkA{dpois}{dpois}} for the Poisson distribution.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
# 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)
\end{ExampleCode}
\end{Examples}

