Random {base}

R Documentation

Random Number Generation

Description

.Random.seed is an integer vector of length 3, containing the “seed” for all random number generation in R. The Wichmann-Hill generator is used which has a cycle length of 6.9536e12 (= prod(p-1)/4 where p is the length 3 vector of primes, below), see p.123 of Applied Statistics (1984) vol.33 which corrects the original article.

Usage

.Random.seed <- c(n1, n2, n3)

Value

.Random.seed == r[1:3], where r[i] is in 1:p[i], and p = (30269, 30307, 30323).

Note

Initially, there is no seed; a new one is created, using “Randomize”. Hence, student exercises will each have different simulation results, by default.

References

B.A. Wichmann and I. D. Hill (1982). Algorithm AS 183: An Efficient and Portable Pseudo-random Number Generator, Applied Statistics, 31, 188-190; Remarks: 34,p.198 and 35, p.89.

A. De Matteis and S. Pagnutti (1993). Long-range Correlation Analysis of the Wichmann-Hill Random Number Generator, Statist. Comput., 3, 67-70.

See Also

runif, rnorm, ....

Examples

runif(1); .Random.seed; runif(1); .Random.seed
## If there is no seed,  a ``random'' new one is created:
rm(.Random.seed); runif(1); .Random.seed

p.WH <- c(30269, 30307, 30323)
a.WH <- c(  171,   172,   170)
R.seed <- function(i.seed = .Random.seed) (a.WH * i.seed) %% p.WH
my.runif1 <- function(i.seed = .Random.seed)
  { ns <- R.seed(i.seed); sum(ns / p.WH) %% 1 }

## This shows how `runif(.)' works, just using  R functions :
rs <- .Random.seed
R.seed(rs); u <- runif(1); .Random.seed; c(u, my.runif1(rs))

---