Rmpfr (version 0.5-3)

chooseMpfr: Binomial Coefficients and Pochhammer Symbol aka Rising Factorial

Description

Compute binomial coefficients, chooseMpfr(a,n) being mathematically the same as choose(a,n), but using high precision (MPFR) arithmetic.

chooseMpfr.all(n) means the vector choose(n, 1:n), using enough bits for exact computation via MPFR. However, chooseMpfr.all() is now deprecated in favor of chooseZ from package gmp, as that is now vectorized.

pochMpfr() computes the Pochhammer symbol or rising factorial, also called the Pochhammer function, Pochhammer polynomial, ascending factorial, rising sequential product or upper factorial,

$$x^{(n)}=x(x+1)(x+2)\cdots(x+n-1)= \frac{(x+n-1)!}{(x-1)!} = \frac{\Gamma(x+n)}{\Gamma(x)}.$$

Usage

chooseMpfr (a, n)
chooseMpfr.all(n, precBits=NULL, k0=1, alternating=FALSE)
pochMpfr(a, n)

Arguments

a

a numeric or mpfr vector.

n

an integer vector; if not of length one, n and a are recycled to the same length.

precBits

integer or NULL for increasing the default precision of the result.

k0

integer scalar

alternating

logical, for chooseMpfr.all(), indicating if alternating sign coefficients should be returned, see below.

Value

• For [object Object],[object Object]

See Also

choose(n,m) (base R) computes the binomial coefficient $C_{n,m}$ which can also be expressed via Pochhammer symbol as $C_{n,m} = (n-m+1)^{(m)}/m!$.

chooseZ from package gmp; for now, factorialMpfr.

For (alternating) binomial sums, directly use sumBinomMpfr, as that is potentially more efficient.

Examples

pochMpfr(100, 4) == 100*101*102*103 # TRUE
a <- 100:110
pochMpfr(a, 10) # exact (but too high precision)
x <- mpfr(a, 70)# should be enough
(px <- pochMpfr(x, 10)) # the same as above (needing only 70 bits)
stopifnot(pochMpfr(a, 10) == px,
          px[1] ==prod(mpfr(100:109, 100)))# used to fail

(c1 <- chooseMpfr(1000:997, 60)) # -> automatic "correct" precision
stopifnot(all.equal(c1, choose(1000:997, 60), tol=1e-12))

## --- Experimenting & Checking
n.set <- c(1:10, 20, 50:55, 100:105, 200:203, 300:303, 500:503,
           699:702, 999:1001)
if(!Rmpfr:::doExtras()) { ## speed up: smaller set
  n. <- n.set[-(1:10)]
  n.set <- c(1:10, n.[ c(TRUE, diff(n.) > 1)])
}
C1 <- C2 <- numeric(length(n.set))
for(i.n in seq_along(n.set)) {
  cat(n <- n.set[i.n],":")
  C1[i.n] <- system.time(c.c <- chooseMpfr.all(n) )[1]
  C2[i.n] <- system.time(c.2 <- chooseMpfr(n, 1:n))[1]
  stopifnot(is.whole(c.c), c.c == c.2,
            if(n > 60) TRUE else all.equal(c.c, choose(n, 1:n), tol = 1e-15))
  cat("[Ok]
")
}
matplot(n.set, cbind(C1,C2), type="b", log="xy",
        xlab = "n", ylab = "system.time(.)  [s]")
legend("topleft", c("chooseMpfr.all(n)", "chooseMpfr(n, 1:n)"),
       pch=as.character(1:2), col=1:2, lty=1:2, bty="n")

## Currently, chooseMpfr.all() is faster only for large n (>= 300)
## That would change if we used C-code for the *.all() version