i_rnorm: Random Number Iterators

Description

These functions each construct an iterator that produces random numbers of various distributions. Each one is a wrapper around a base R function.

Usage

i_rnorm(
  n,
  mean = 0,
  sd = 1,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
irnorm(
  n,
  mean = 0,
  sd = 1,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
i_runif(
  n,
  min = 0,
  max = 1,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
irunif(
  n,
  min = 0,
  max = 1,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
i_rbinom(
  n,
  size,
  prob,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
irbinom(
  n,
  size,
  prob,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
i_rpois(
  n,
  lambda,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
irpois(
  n,
  lambda,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
i_rnbinom(
  n,
  size,
  prob,
  mu,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
irnbinom(
  n,
  size,
  prob,
  mu,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
i_sample(
  x,
  size,
  replace = FALSE,
  prob = NULL,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
isample(
  x,
  size,
  replace = FALSE,
  prob = NULL,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
i_runif(
  n,
  min = 0,
  max = 1,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)
irunif(
  n,
  min = 0,
  max = 1,
  count = Inf,
  independent = !missing(seed) || !missing(kind),
  seed = NULL,
  kind = NULL,
  normal.kind = NULL,
  sample.kind = NULL
)

Value

An iterator that is a wrapper around the corresponding random number generator function.

Arguments

n: How many samples to compute per call; see e.g. rnorm.
mean: see rnorm.
sd: see rnorm.
count: number of times that the iterator will fire. If not specified, it will fire values forever.
independent: If TRUE, this iterator will keep its own private random state, so that its output is reproducible and independent of anything else in the program; this comes at some performance cost. Default is FALSE unless seed or kind are given. If independent=TRUE but neither seed nor kind are specified, we will use the "L'Ecuyer-CMRG" generator with a seed value taken from a package-private instance of iRNGStream.
seed: A specific seed value for reproducibility. If given, independent=TRUE is implied. This can be a single number (which will be passed to set.seed(seed, kind, normal.kind, sample.kind); it can also be a vector containing a complete, valid state for .Random.seed. If the latter, arguments kind, etc. are ignored.
kind: Which random number algorithm to use; passed along to set.seed, If given, independent=TRUE is implied.
normal.kind: Passed along to set.seed.
sample.kind: Passed along to set.seed.
min: see runif.
max: see runif.
size: see e.g. rbinom.
prob: see e.g. rbinom.
lambda: see rpois.
mu: see rnbinom.
x: see isample.
replace: see isample.

Details

Originally from the iterators package.

Examples

Run this code


# create an iterator that returns three random numbers
it <- irnorm(1, count = 3)
nextOr(it)
nextOr(it)
nextOr(it)
nextOr(it, NULL)

# iterators created with a specific seed will make reproducible values
it <- irunif(n=1, seed=314, kind="L'Ecuyer-CMRG")
nextOr(it) # 0.4936700
nextOr(it) # 0.5103891
nextOr(it) # 0.2338745

# the iRNGStream produces a sequence of well separated seed values,
rng.seeds <- iRNGStream(313)
it1 <- isample(c(0, 1), 1, seed=nextOr(rng.seeds))
it2 <- isample(c(0, 1), 1, seed=nextOr(rng.seeds))
it3 <- isample(c(0, 1), 1, seed=nextOr(rng.seeds))
take(it1, 5, "numeric") # 0 1 0 0 1
take(it2, 5, "numeric") # 0 1 0 0 0
take(it3, 5, "numeric") # 0 0 0 1 1