.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, \dots)RNGkind(kind = NULL, normal.kind = NULL)
RNGversion(vstr)
set.seed(seed, kind = NULL, normal.kind = NULL)
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.
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.
a single value, interpreted as an integer, or NULL
(see ‘Details’).
a character string containing a version number,
e.g., "1.6.2"
integer code in 0:k for the above kind.
integers. See the details for how many are required
(which depends on rng.kind).
.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 selected before the call, invisibly if either
argument is not NULL. A type starts a session as the default,
and is selected either by a call to RNGkind or by setting
.Random.seed in the workspace.
RNGversion returns the same information as RNGkind about
the defaults in a specific R version.
set.seed returns NULL, invisibly.
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-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 \(\approx 4.6\times 10^{18}\) 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-2002":A 32-bit integer GFSR using lagged Fibonacci sequences with subtraction. That is, the recurrence used is $$X_j = (X_{j-100} - X_{j-37}) \bmod 2^{30}% $$ 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":An earlier version from Knuth (1997).
The 2002 version was 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.
Initialization of this generator is done in interpreted R code and so takes a short but noticeable time.
"L'Ecuyer-CMRG":A ‘combined multiple-recursive generator’ from L'Ecuyer (1999), each element of which is a feedback multiplicative generator with three integer elements: thus the seed is a (signed) integer vector of length 6. The period is around \(2^{191}\).
The 6 elements of the seed are internally regarded as 32-bit
unsigned integers. Neither the first three nor the last three
should be all zero, and they are limited to less than
4294967087 and 4294944443 respectively.
This is not particularly interesting of itself, but provides the basis for the multiple streams used in package parallel.
"user-supplied":Use a user-supplied generator. See Random.user for
details.
normal.kind can be "Kinderman-Ramage",
"Buggy Kinderman-Ramage" (not for set.seed),
"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 1.7.0 (now called "Buggy") had several
approximation errors and should only be used for reproduction of old
results. The "Box-Muller" generator is stateful as pairs of
normals are generated and returned sequentially. The state is reset
whenever it is selected (even if it is the current normal generator)
and when kind is changed.
set.seed uses a 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"). There is no
guarantee that different values of seed will seed the RNG
differently, although any exceptions would be extremely rare. If
called with seed = NULL it re-initializes (see ‘Note’)
as if no seed had yet been set.
The use of kind = NULL or normal.kind = NULL in
RNGkind or set.seed selects the currently-used
generator (including that used in the previous session if the
workspace has been restored): if no generator has been used it selects
"default".
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.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
The New S Language.
Wadsworth & Brooks/Cole. (set.seed, storing in .Random.seed.)
Box, G. E. P. and Muller, M. E. (1958) A note on the generation of normal random deviates. Annals of Mathematical Statistics 29, 610--611.
De Matteis, A. and Pagnutti, S. (1993) Long-range Correlation Analysis of the Wichmann-Hill Random Number Generator, Statist. Comput., 3, 67--70.
Kinderman, A. J. and Ramage, J. G. (1976) Computer generation of normal random variables. Journal of the American Statistical Association 71, 893-896.
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.
L'Ecuyer, P. (1999) Good parameters and implementations for combined multiple recursive random number generators. Operations Research 47, 159--164.
Marsaglia, G. (1997) A random number generator for C. Discussion
paper, posting on Usenet newsgroup sci.stat.math on
September 29, 1997.
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 formerly at http://www.math.keio.ac.jp/~matumoto/emt.html.
Now see http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/VERSIONS/C-LANG/c-lang.html.
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.)
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.
sample for random sampling with and without replacement.
Distributions for functions for random-variate generation from standard distributions.