Accurate Incomplete Beta / Beta Probabilities For Integer Shapes
For integers a, b, I(x; a,b) aka
pbeta(x, a,b) is a polynomial in x with rational coefficients,
and hence arbitarily accurately computable.
pbetaI(q, shape1, shape2, ncp = 0, lower.tail = TRUE, log.p = FALSE,
       precBits = NULL,
       useRational = !log.p && !is.mpfr(q) && is.null(precBits),
       rnd.mode = c("N","D","U","Z","A"))| q | called x, above; vector of quantiles, in [0,1]; can
be  | 
| shape1, shape2 | the positive Beta “shape” parameters, called a, b, above. Must be integer valued for this function. | 
| ncp | unused, only for compatibility with  | 
| lower.tail | logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x]. | 
| log.p | logical; if TRUE, probabilities p are given as log(p). | 
| precBits | the precision (in number of bits) to be used in
 | 
| useRational | optional  | 
| rnd.mode | a 1-letter string specifying how rounding
should happen at C-level conversion to MPFR, see  | 
an "mpfr" vector of the same length as q.
For upper tail probabilities, i.e., when lower.tail=FALSE,
we may need large precBits, because the implicit or explicit
1 - P computation suffers from severe cancellation.
Martin Maechler
x <- (0:12)/16 # not all the way up ..
a <- 7; b <- 788
p.  <- pbetaI(x, a, b) ## a bit slower:
system.time(
pp  <- pbetaI(x, a, b, precBits = 2048)
) # 0.23 -- 0.50 sec
## Currently, the lower.tail=FALSE  are computed "badly":
lp  <- log(pp)    ## = pbetaI(x, a, b, log.p=TRUE)
lIp <- log1p(-pp) ## = pbetaI(x, a, b, lower.tail=FALSE, log.p=TRUE)
 Ip <- 1 - pp     ## = pbetaI(x, a, b, lower.tail=FALSE)
if(Rmpfr:::doExtras()) { ## somewhat slow
   stopifnot(
     all.equal(lp,  pbetaI(x, a, b, precBits = 2048, log.p=TRUE)),
     all.equal(lIp, pbetaI(x, a, b, precBits = 2048, lower.tail=FALSE, log.p=TRUE),
               tol = 1e-230),
     all.equal( Ip, pbetaI(x, a, b, precBits = 2048, lower.tail=FALSE))
   )
}
rErr <- function(approx, true, eps = 1e-200) {
    true <- as.numeric(true) # for "mpfr"
    ifelse(Mod(true) >= eps,
           ## relative error, catching '-Inf' etc :
	   ifelse(true == approx, 0, 1 - approx / true),
           ## else: absolute error (e.g. when true=0)
	   true - approx)
}
rErr(pbeta(x, a, b), pp)
rErr(pbeta(x, a, b, lower=FALSE), Ip)
rErr(pbeta(x, a, b, log = TRUE),  lp)
rErr(pbeta(x, a, b, lower=FALSE, log = TRUE),  lIp)
a.EQ <- function(..., tol=1e-15) all.equal(..., tolerance=tol)
stopifnot(
  a.EQ(pp,  pbeta(x, a, b)),
  a.EQ(lp,  pbeta(x, a, b, log.p=TRUE)),
  a.EQ(lIp, pbeta(x, a, b, lower.tail=FALSE, log.p=TRUE)),
  a.EQ( Ip, pbeta(x, a, b, lower.tail=FALSE))
 )
## When 'q' is a  bigrational (i.e., class "bigq", package 'gmp'), everything
## is computed *exactly* with bigrational arithmetic:
(q4 <- as.bigq(1, 2^(0:4)))
pb4 <- pbetaI(q4, 10, 288, lower.tail=FALSE)
stopifnot( is.bigq(pb4) )
mpb4 <- as(pb4, "mpfr")
mpb4[1:2]
getPrec(mpb4) # 128 349 1100 1746 2362
(pb. <- pbeta(asNumeric(q4), 10, 288, lower.tail=FALSE))
stopifnot(mpb4[1] == 0,
          all.equal(mpb4, pb., tol=4e-15))
qbetaI. <- function(p, shape1, shape2, ncp = 0, lower.tail = TRUE, log.p = FALSE,
    precBits = NULL, rnd.mode = c("N", "D", "U", "Z", "A"),
    tolerance = 1e-20, ...)
{
    if(is.na(a <- as.integer(shape1))) stop("a = shape1 is not coercable to finite integer")
    if(is.na(b <- as.integer(shape2))) stop("b = shape2 is not coercable to finite integer")
    unirootR(function(q) pbetaI(q, a, b, lower.tail=lower.tail, log.p=log.p,
                                precBits=precBits, rnd.mode=rnd.mode) - p,
             interval = if(log.p) c(-double.xmax, 0) else 0:1,
             tol = tolerance, ...)
} # end{qbetaI}
(p <- 1 - mpfr(1,128)/20) # 'p' must be high precision
q95.1.3 <- qbetaI.(p, 1,3, tolerance = 1e-29) # -> ~29 digits accuracy
str(q95.1.3) ; roundMpfr(q95.1.3$root, precBits = 29 * log2(10))
## relative error is really small:
(relE <- asNumeric(1 - pbetaI(q95.1.3$root, 1,3) / p))
stopifnot(abs(relE) < 1e-28)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.