Random {base} | R Documentation |

`.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.

.Random.seed <- c(rng.kind, n1, n2, ...) save.seed <- .Random.seed RNGkind(kind = NULL, normal.kind = NULL) RNGversion(vstr) set.seed(seed, kind = NULL)

`kind` |
character or `NULL` . If `kind` is a character
string, set R's RNG to the kind desired. If it is `NULL` ,
return the currently used RNG. Use `"default"` to return to the
R default. |

`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. |

`seed` |
a single value, interpreted as an integer. |

`vstr` |
a character string containing a version number,
e.g., `"1.6.2"` |

`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` ). |

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*6.9536e12*(=`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-Duper from the 70's. This is the original
version which does
*not*pass the MTUPLE test of the Diehard battery. It has a period of*about 4.6*10^18*for most initial seeds. The seed is two integers (all values allowed for the first seed: the second must be odd).We use the implementation by Reeds et al. (1982–84).

The two seeds are the Tausworthe and congruence long integers, respectively. A one-to-one mapping to S's

`.Random.seed[1:12]`

is possible but we will not publish one, not least as this generator is**not**exactly the same as that in recent versions of S-PLUS. `"Mersenne-Twister":`

- From Matsumoto and Nishimura (1998). A twisted GFSR with period
*2^19937 - 1*and equidistribution in 623 consecutive dimensions (over the whole period). The “seed” is a 624-dimensional set of 32-bit integers plus a current position in that set. `"Knuth-TAOCP":`

- From Knuth (1997). A GFSR using lagged Fibonacci sequences with
subtraction. That is, the recurrence used is
*X[j] = (X[j-100] - X[j-37]) mod 2^30*and the “seed” is the set of the 100 last numbers (actually recorded as 101 numbers, the last being a cyclic shift of the buffer). The period is around

*2^129*. `"Knuth-TAOCP-2002":`

- The 2002 version which not backwards compatible with the earlier
version: the initialization of the GFSR from the seed was altered.
**R**did not allow you to choose consecutive seeds, the reported ‘weakness’, and already scrambled the seeds. `"user-supplied":`

- Use a user-supplied generator. See
`Random.user`

for details.

`normal.kind`

can be `"Kinderman-Ramage"`

,
`"Buggy Kinderman-Ramage"`

,
`"Ahrens-Dieter"`

, `"Box-Muller"`

, `"Inversion"`

(the
default), or
`"user-supplied"`

. (For inversion, see the reference in
`qnorm`

.)
The Kinderman-Ramage generator used in versions prior
to 1.7.1 had several approximation errors and should only be used for
reproduction of older results.

`set.seed`

uses its single integer argument to set as many seeds
as are required. It is intended as a simple way to get quite different
seeds by specifying small integer arguments, and also as a way to get
valid seed sets for the more complicated methods (especially
`"Mersenne-Twister"`

and `"Knuth-TAOCP"`

).

`.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`

).

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`

returns a two-element character vector of the RNG and
normal kinds in use *before* the call, invisibly if either
argument is not `NULL`

. `RNGversion`

returns the same
information.

`set.seed`

returns `NULL`

, invisibly.

Initially, there is no seed; a new one is created from the current time when one is required. Hence, different sessions will give different simulation results, by default.

`.Random.seed`

saves the seed set for the uniform random-number
generator, at least for the system generators. It does not
necessarily save the state of other generators, and in particular does
not save the state of the Box–Muller normal generator. If you want
to reproduce work later, call `set.seed`

rather than set
`.Random.seed`

.

As from **R** 1.8.0, `.Random.seed`

is only looked for in the user's
workspace.

All the supplied uniform generators return 32-bit integer values that
are converted to doubles, so they take at most *2^32*
distinct values and long runs will return duplicated values.

of RNGkind: Martin Maechler. Current implementation, B. D. Ripley

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
*The New S Language*.
Wadsworth & Brooks/Cole. (`set.seed`

, storing in `.Random.seed`

.)

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

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

Marsaglia, G. (1997) *A random number generator for C.* Discussion
paper, posting on Usenet newsgroup `sci.stat.math`

on
September 29, 1997.

Reeds, J., Hubert, S. and Abrahams, M. (1982–4) C implementation of SuperDuper, University of California at Berkeley. (Personal communication from Jim Reeds to Ross Ihaka.)

Marsaglia, G. and Zaman, A. (1994) Some portable very-long-period
random number generators. *Computers in Physics*, **8**,
117–121.

Matsumoto, M. and Nishimura, T. (1998)
Mersenne Twister: A 623-dimensionally equidistributed uniform
pseudo-random number generator,
*ACM Transactions on Modeling and Computer Simulation*,
**8**, 3–30.

Source code at http://www.math.keio.ac.jp/~matumoto/emt.html.

Knuth, D. E. (1997)
*The Art of Computer Programming.* Volume 2, third edition.

Source code at http://www-cs-faculty.stanford.edu/~knuth/taocp.html.

Knuth, D. E. (2002)
*The Art of Computer Programming.* Volume 2, third edition, ninth
printing.

See http://Sunburn.Stanford.EDU/~knuth/news02.html.

Kinderman, A. J. and Ramage, J. G. (1976)
Computer generation of normal random variables.
*Journal of the American Statistical Association* **71**,
893-896.

Ahrens, J.H. and Dieter, U. (1973)
Extensions of Forsythe's method for random sampling from the normal
distribution.
*Mathematics of Computation* **27**, 927-937.

Box, G.E.P. and Muller, M.E. (1958) A note on the generation of normal
random deviates. *Annals of Mathmatical Statistics* **29**,
610–611.

## 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] 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 } 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 ok <- RNGkind() RNGkind("Super")#matches "Super-Duper" RNGkind() .Random.seed # new, corresponding to Super-Duper ## Reset: RNGkind(ok[1]) ## ---- sum(duplicated(runif(1e6))) # around 110 ## and we would expect about almost sure duplicates beyond about qbirthday(1-1e-6, classes=2e9) # 235,000

[Package *base* version 2.1.0 Index]