rng: Getting/Setting RNGs
In rngtools: Utility functions for working with Random Number Generators

Description Usage Arguments Details Value Methods See Also Examples

getRNG returns the Random Number Generator (RNG) settings used for computing an object, using a suitable .getRNG S4 method to extract these settings. For example, in the case of objects that result from multiple model fits, it would return the RNG settings used to compute the best fit.

hasRNG tells if an object has embedded RNG data.

.getRNG is an S4 generic that extract RNG settings from a variety of object types. Its methods define the workhorse functions that are called by getRNG.

getRNG1 is defined to provide separate access to the RNG settings as they were at the very beginning of a whole computation, which might differ from the RNG settings returned by getRNG, that allows to reproduce the result only.

nextRNG returns the RNG settings as they would be after seeding with object.

setRNG set the current RNG with a seed, using a suitable .setRNG method to set these settings.

.setRNG is an S4 generic that sets the current RNG settings, from a variety of specifications. Its methods define the workhorse functions that are called by setRNG.

  getRNG(object, ..., num.ok = FALSE, extract = TRUE,
    recursive = TRUE)

  hasRNG(object)

  .getRNG(object, ...)

  getRNG1(object, ...)

  nextRNG(object, ..., ndraw = 0L)

  setRNG(object, ..., verbose = FALSE, check = TRUE)

  .setRNG(object, ...)

`object`	an R object from which RNG settings can be extracted, e.g. an integer vector containing a suitable value for `.Random.seed` or embedded RNG data, e.g., in S3/S4 slot `rng` or `rng$noise`.
`...`	extra arguments to allow extension and passed to a suitable S4 method `.getRNG` or `.setRNG`.
`num.ok`	logical that indicates if single numeric (not integer) RNG data should be considered as a valid RNG seed (`TRUE`) or passed to `set.seed` into a proper RNG seed (`FALSE`) (See details and examples).
`extract`	logical that indicates if embedded RNG data should be looked for and extracted (`TRUE`) or if the object itself should be considered as an RNG specification.
`recursive`	logical that indicates if embedded RNG data should be extracted recursively (`TRUE`) or only once (`FASE`).
`ndraw`	number of draws to perform before returning the RNG seed.
`check`	logical that indicates if only valid RNG kinds should be accepted, or if invalid values should just throw a warning. Note that this argument is used only on R >= 3.0.2.
`verbose`	a logical that indicates if the new RNG settings should be displayed.

This function handles single number RNG specifications in the following way:

integers: Return them unchanged, considering them as encoded RNG kind specification (see RNG). No validity check is performed.
real numbers: If num.ok=TRUE return them unchanged. Otherwise, consider them as (pre-)seeds and pass them to set.seed to get a proper RNG seed. Hence calling getRNG(1234) is equivalent to set.seed(1234); getRNG() (See examples).

Think of a sequence of separate computations, from which only one result is used for the result (e.g. the one that maximises a likelihood): getRNG1 would return the RNG settings to reproduce the complete sequence of computations, while getRNG would return the RNG settings necessary to reproduce only the computation whose result has maximum likelihood.

getRNG, getRNG1, nextRNG and setRNG usually return an integer vector of length > 2L, like .Random.seed.

getRNG and getRNG1 return NULL if no RNG data was found.

setRNG invisibly returns the old RNG settings as they were before changing them.

.getRNG

signature(object = "ANY"): Default method that tries to extract RNG information from object, by looking sequentially to a slot named 'rng', a slot named 'rng.seed' or an attribute names 'rng'.

It returns NULL if no RNG data was found.

.getRNG

signature(object = "missing"): Returns the current RNG settings.

.getRNG

signature(object = "list"): Method for S3 objects, that aims at reproducing the behaviour of the function getRNG of the package getRNG.

It sequentially looks for RNG data in elements 'rng', noise$rng if element 'noise' exists and is a list, or in attribute 'rng'.

.getRNG

signature(object = "numeric"): Method for numeric vectors, which returns the object itself, coerced into an integer vector if necessary, as it is assumed to already represent a value for .Random.seed.

getRNG1

signature(object = "ANY"): Default method that is identical to getRNG(object, ...).

.setRNG

signature(object = "character"): Sets the RNG to kind object, assuming is a valid RNG kind: it is equivalent to RNGkind(object, .... All arguments in ... are passed to RNGkind.

.setRNG

signature(object = "numeric"): Sets the RNG settings using object directly the new value for .Random.seed or to initialise it with set.seed.

.Random.seed, showRNG

# get current RNG settings
s <- getRNG()
head(s)
showRNG(s)

# get RNG from a given single numeric seed
s1234 <- getRNG(1234)
head(s1234)
showRNG(s1234)
# this is identical to the RNG seed as after set.seed()
set.seed(1234)
identical(s1234, .Random.seed)
# but if num.ok=TRUE the object is returned unchanged
getRNG(1234, num.ok=TRUE)

# single integer RNG data = encoded kind
head(getRNG(1L))

# embedded RNG data
s <- getRNG(list(1L, rng=1234))
identical(s, s1234)
# test for embedded RNG data
hasRNG(1)
hasRNG( structure(1, rng=1:3) )
hasRNG( list(1, 2, 3) )
hasRNG( list(1, 2, 3, rng=1:3) )
hasRNG( list(1, 2, 3, noise=list(1:3, rng=1)) )
head(nextRNG())
head(nextRNG(1234))
head(nextRNG(1234, ndraw=10))
obj <- list(x=1000, rng=123)
setRNG(obj)
rng <- getRNG()
runif(10)
set.seed(123)
rng.equal(rng)
# set RNG kind
old <- setRNG('Marsaglia')
# restore
setRNG(old)
# directly set .Random.seed
rng <- getRNG()
r <- runif(10)
setRNG(rng)
rng.equal(rng)

# initialise from a single number (<=> set.seed)
setRNG(123)
rng <- getRNG()
runif(10)
set.seed(123)
rng.equal(rng)