# Beta

##### The Beta Distribution

Density, distribution function, quantile function and random
generation for the Beta distribution with parameters `shape1`

and
`shape2`

(and optional non-centrality parameter `ncp`

).

- Keywords
- distribution

##### Usage

```
dbeta(x, shape1, shape2, ncp = 0, log = FALSE)
pbeta(q, shape1, shape2, ncp = 0, lower.tail = TRUE, log.p = FALSE)
qbeta(p, shape1, shape2, ncp = 0, lower.tail = TRUE, log.p = FALSE)
rbeta(n, shape1, shape2, ncp = 0)
```

##### 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. - shape1, shape2
- non-negative parameters of the Beta distribution.
- ncp
- non-centrality parameter.
- 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, $P[X > x]$.

##### Details

The Beta distribution with parameters `shape1`

$= a$ and
`shape2`

$= b$ has density
$$f(x)=\frac{\Gamma(a+b)}{\Gamma(a)\Gamma(b)}{x}^{a-1} {(1-x)}^{b-1}%
$$
for $a > 0$, $b > 0$ and $0 \le x \le 1$
where the boundary values at $x=0$ or $x=1$ are defined as
by continuity (as limits).
The mean is $a/(a+b)$ and the variance is $ab/((a+b)^2 (a+b+1))$.
These moments and all distributional properties can be defined as
limits (leading to point masses at 0, 1/2, or 1) when $a$ or
$b$ are zero or infinite, and the corresponding
`[dpqr]beta()`

functions are defined correspondingly.

`pbeta`

is closely related to the incomplete beta function. As
defined by Abramowitz and Stegun 6.6.1
$$B_x(a,b) = \int_0^x t^{a-1} (1-t)^{b-1} dt,$$
and 6.6.2 $I_x(a,b) = B_x(a,b) / B(a,b)$ where
$B(a,b) = B_1(a,b)$ is the Beta function (`beta`

).

$I_x(a,b)$ is `pbeta(x, a, b)`

.

The noncentral Beta distribution (with `ncp`

$ = \lambda$)
is defined (Johnson *et al*, 1995, pp.\ifelse{latex}{\out{~}}{ } 502) as the distribution of
$X/(X+Y)$ where $X ~ chi^2_2a(\lambda)$
and $Y ~ chi^2_2b$.

##### Value

`dbeta`

gives the density, `pbeta`

the distribution
function, `qbeta`

the quantile function, and `rbeta`

generates random deviates.Invalid arguments will result in return value `NaN`

, with a warning.The length of the result is determined by `n`

for
`rbeta`

, 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.
##### Note

Supplying `ncp = 0`

uses the algorithm for the non-central
distribution, which is not the same algorithm used if `ncp`

is
omitted. This is to give consistent behaviour in extreme cases with
values of `ncp`

very near zero.

##### Source

The central `dbeta`

is based on a binomial probability, using code
contributed by Catherine Loader (see `dbinom`

) if either
shape parameter is larger than one, otherwise directly from the definition.
The non-central case is based on the derivation as a Poisson
mixture of betas (Johnson *et al*, 1995, pp.\ifelse{latex}{\out{~}}{ } 502--3). The central `pbeta`

uses a C translation (and enhancement for
`log_p = TRUE`

) of Didonato, A. and Morris, A., Jr, (1992)
Algorithm 708: Significant digit computation of the incomplete beta
function ratios,
*ACM Transactions on Mathematical Software*, **18**, 360--373.
(See also
Brown, B. and Lawrence Levy, L. (1994)
Certification of algorithm 708: Significant digit computation of the
incomplete beta,
*ACM Transactions on Mathematical Software*, **20**, 393--397.) The non-central `pbeta`

uses a C translation of Lenth, R. V. (1987) Algorithm AS226: Computing noncentral beta
probabilities. *Appl. Statist*, **36**, 241--244,
incorporating
Frick, H. (1990)'s AS R84, *Appl. Statist*, **39**, 311--2,
and
Lam, M.L. (1995)'s AS R95, *Appl. Statist*, **44**, 551--2. This computes the lower tail only, so the upper tail suffers from
cancellation and a warning will be given when this is likely to be
significant. The central case of `qbeta`

is based on a C translation of Cran, G. W., K. J. Martin and G. E. Thomas (1977).
Remark AS R19 and Algorithm AS 109,
*Applied Statistics*, **26**, 111--114,
and subsequent remarks (AS83 and correction). The central case of `rbeta`

is based on a C translation of R. C. H. Cheng (1978).
Generating beta variates with nonintegral shape parameters.
*Communications of the ACM*, **21**, 317--322.

##### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
*The New S Language*.
Wadsworth & Brooks/Cole.

Abramowitz, M. and Stegun, I. A. (1972)
*Handbook of Mathematical Functions.* New York: Dover.
Chapter 6: Gamma and Related Functions.

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995)
*Continuous Univariate Distributions*, volume 2, especially
chapter 25. Wiley, New York.

##### See Also

Distributions for other standard distributions.

`beta`

for the Beta function.

##### Examples

`library(stats)`

```
x <- seq(0, 1, length = 21)
dbeta(x, 1, 1)
pbeta(x, 1, 1)
## Visualization, including limit cases:
pl.beta <- function(a,b, asp = if(isLim) 1, ylim = if(isLim) c(0,1.1)) {
if(isLim <- a == 0 || b == 0 || a == Inf || b == Inf) {
eps <- 1e-10
x <- c(0, eps, (1:7)/16, 1/2+c(-eps,0,eps), (9:15)/16, 1-eps, 1)
} else {
x <- seq(0, 1, length = 1025)
}
fx <- cbind(dbeta(x, a,b), pbeta(x, a,b), qbeta(x, a,b))
f <- fx; f[fx == Inf] <- 1e100
matplot(x, f, ylab="", type="l", ylim=ylim, asp=asp,
main = sprintf("[dpq]beta(x, a=%g, b=%g)", a,b))
abline(0,1, col="gray", lty=3)
abline(h = 0:1, col="gray", lty=3)
legend("top", paste0(c("d","p","q"), "beta(x, a,b)"),
col=1:3, lty=1:3, bty = "n")
invisible(cbind(x, fx))
}
pl.beta(3,1)
pl.beta(2, 4)
pl.beta(3, 7)
pl.beta(3, 7, asp=1)
pl.beta(0, 0) ## point masses at {0, 1}
pl.beta(0, 2) ## point mass at 0 ; the same as
pl.beta(1, Inf)
pl.beta(Inf, 2) ## point mass at 1 ; the same as
pl.beta(3, 0)
pl.beta(Inf, Inf)# point mass at 1/2
```

*Documentation reproduced from package stats, version 3.2.2, License: Part of R 3.2.2*