The R Project SVN R

\name{Random}
\title{Random Number Generation}
\usage{
  .Random.seed <- c(n1,n2,n3)
}
\alias{.Random.seed}
\description{
\code{.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
(= \code{prod(p-1)/4} where \code{p} is the length 3 vector of primes, below),
see p.123 of Applied Statistics (1984) vol.33 which corrects the original
article.
}
\value{
\code{.Random.seed == r[1:3]}, where  \code{r[i]} is in \code{1:p[i]},
and \code{p = (30269, 30307, 30323)}.
}
\references{
B.A. Wichmann and I. D. Hill (1982).
\emph{Algorithm AS 183: An Efficient and Portable Pseudo-random Number
Generator}, Applied Statistics, \textbf{31}, 188-190; Remarks: \textbf{34},p.198
and \textbf{35}, p.89.

A. De Matteis and S. Pagnutti (1993). \emph{Long-range Correlation
Analysis of the Wichmann-Hill Random Number Generator}, Statist. Comput.,
\textbf{3}, 67-70.
}
\note{
Initially, there is no seed;  a new one is created, using "Randomize".
Hence, student exercises will each have different simulation results, by
default.
}
\seealso{
\code{\link{runif}}, \code{\link{rnorm}}, \ldots.
}
\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))
}