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.
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.
Details
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.
Value
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.
Methods
.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.
See Also
.Random.seed, showRNG
Examples
# 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)