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# invbeta()

Usage:
 ```invbeta(P, alpha, beta [,upper:T or lower:F]), P, alpha and beta REAL, elements of P between 0 and 1, those of alpha and beta > 0 ```

Keywords: probabilities, random numbers
```invbeta(p,a,b) computes the pth quantile (100*p percent point) of the
beta distribution with parameters a and b.

The elements of p must be between 0 and 1, inclusive, and the elements
of a and b must be positive REAL numbers.

If p, a, and b are not all scalars (single numbers), all non-scalar
arguments must have must have the same size and shape and any scalar
arguments are used to compute all the elements of the result.

invbeta(p,a,b,upper:T) and invbeta(p,a,b,lower:F) compute an upper tail
quantile mathematically equivalent to invbeta(1 - p,a,b), but more
accurate when p is very close to 1.

invbeta() is the inverse of cumbeta().

invbeta(runi(n),a,b) will generate a random sample of size n from a beta
distribution .

You can use invbeta() to compute an "exact" confidence for a probability
p based on an observed value x_obs of a binomial random variable with n
trials and P(success) = p.

Cmd> n <- 19; x_obs <- 11; alpha <- .05 # 95% confidence

Cmd> p_l <- invbeta(alpha/2,x_obs,n - x_obs + 1)

Cmd> p_u <- invbeta(alpha/2,x_obs + 1,n - x_obs,upper:T)

Cmd> vector(p_l,p_u) # exact confidence limits
(1)       0.335     0.79748

Cmd> vector(cumbin(x_obs,n,p_u),cumbin(x_obs,n,p_l,upper:T)) #check
(1)       0.025       0.025

Cmd> invbeta(.975,3,4) # P(x <= .77722) = .975
(1)      0.77722

Cmd> invbeta(.025,3,4,upper:T) # P(x >= .77722) = .025
(1)      0.77722