Random

R Base 3.6.2

.Random.seed is an integer vector, containing the random number generator (RNG) state for random number generation in R. It can be saved and restored, but should not be altered by the user. RNGkind is a more friendly interface to query or set the kind of RNG in use. RNGversion can be used to set the random generators as they were in an earlier R version (for reproducibility). set.seed is the recommended way to specify seeds.

Syntax

<code>.Random.seed &lt;- c(rng.kind, n1, n2, \dots)</code><p></p><p>RNGkind(kind = NULL, normal.kind = NULL, sample.kind = NULL)
RNGversion(vstr)
set.seed(seed, kind = NULL, normal.kind = NULL, sample.kind = NULL)</p>

Arguments

Parameter	Description
`kind`	character or NULL. If kind is a character string, set R's RNG to the kind desired. Use "default" to return to the R default. See ‘Details’ for the interpretation of NULL.
`normal.kind`	character string or NULL. If it is a character string, set the method of Normal generation. Use "default" to return to the R default. NULL makes no change.
`sample.kind`	character string or NULL. If it is a character string, set the method of discrete uniform generation (used in sample, for instance). Use "default" to return to the R default. NULL makes no change.
`seed`	a single value, interpreted as an integer, or NULL (see ‘Details’).
`vstr`	a character string containing a version number, e.g., "1.6.2". The default RNG configuration of the current R version is used if vstr is greater than the current version.
`rng.kind`	integer code in 0:k for the above kind.
`n1, n2, …`	integers. See the details for how many are required (which depends on rng.kind).

Return Value

.Random.seed is an integer vector whose first element codes the kind of RNG and normal generator. The lowest two decimal digits are in 0:(k-1) where k is the number of available RNGs. The hundreds represent the type of normal generator (starting at 0), and the ten thousands represent the type of discrete uniform sampler. In the underlying C, .Random.seed[-1] is unsigned; therefore in R .Random.seed[-1] can be negative, due to the representation of an unsigned integer by a signed integer. RNGkind

Details

The currently available RNG kinds are given below. kind is partially matched to this list. The default is "Mersenne-Twister". "Wichmann-Hill"The seed, .Random.seed[-1] == r[1:3] is an integer vector of length 3, where each r[i] is in 1:(p[i] - 1), where p is the length 3 vector of primes, p = (30269, 30307, 30323). The Wichmann--Hill generator has a cycle length of \(6.9536 \times 10^{12}\) (= prod(p-1)/4, see Applied Statistics (1984) 33, 123 which corrects the original article)."Marsaglia-Multicarry":A multiply-with-carry RNG is used, as recommended by George Marsaglia in his post to the mailing list sci.stat.math. It has a period of more than \(2^{60}\) and has passed all tests (according to Marsaglia). The seed is two integers (all values allowed)."Super-Duper":Marsaglia's famous Super

Examples

Example

# NOT RUN {
require(stats)

## Seed the current RNG, i.e., set the RNG status
set.seed(42); u1 <- runif(30)
set.seed(42); u2 <- runif(30) # the same because of identical RNG status:
stopifnot(identical(u1, u2))

# }
# NOT RUN {
## the default random seed is 626 integers, so only print a few
 runif(1); .Random.seed[1:6]; runif(1); .Random.seed[1:6]
 ## If there is no seed, a "random" new one is created:
 rm(.Random.seed); runif(1); .Random.seed[1:6]
# }
# NOT RUN {
ok <- RNGkind()
RNGkind("Wich")  # (partial string matching on 'kind')

## This shows how 'runif(.)' works for Wichmann-Hill,
## using only R functions:

p.WH <- c(30269, 30307, 30323)
a.WH <- c(  171,   172,   170)
next.WHseed <- function(i.seed = .Random.seed[-1])
  { (a.WH * i.seed) %% p.WH }
my.runif1 <- function(i.seed = .Random.seed)
  { ns <- next.WHseed(i.seed[-1]); sum(ns / p.WH) %% 1 }
set.seed(1998-12-04)# (when the next lines were added to the souRce)
rs <- .Random.seed
(WHs <- next.WHseed(rs[-1]))
u <- runif(1)
stopifnot(
 next.WHseed(rs[-1]) == .Random.seed[-1],
 all.equal(u, my.runif1(rs))
)

## ----
.Random.seed
RNGkind("Super") # matches  "Super-Duper"
RNGkind()
.Random.seed # new, corresponding to  Super-Duper

## Reset:
RNGkind(ok[1])

RNGversion(getRversion()) # the default version for this R version

## ----
sum(duplicated(runif(1e6))) # around 110 for default generator
## and we would expect about almost sure duplicates beyond about
qbirthday(1 - 1e-6, classes = 2e9) # 235,000
# }