| sample {base} | R Documentation |
sample takes a sample of the specified size from the elements
of x using either with or without replacement.
sample(x, size, replace = FALSE, prob = NULL)
sample.int(n, size, replace = FALSE, prob = NULL)
x |
Either a (numeric, complex, character or logical) vector of more than one element from which to choose, or a positive integer. |
n |
a non-negaiive integer, the number of items to choose from. |
size |
positive integer giving the number of items to choose. |
replace |
Should sampling be with replacement? |
prob |
A vector of probability weights for obtaining the elements of the vector being sampled. |
If x has length 1, is numeric (in the sense of
is.numeric) and x >= 1, sampling via
sample takes place from
1:x. Note that this convenience feature may lead to
undesired behaviour when x is of varying length
sample(x). See the resample() example below.
By default size is equal to length(x)
so that sample(x) generates a random permutation
of the elements of x (or 1:x).
The optional prob argument can be used to give a vector
of weights for obtaining the elements of the vector being
sampled. They need not sum to one, but they should be nonnegative
and not all zero. If replace is true, Walker's alias method
(Ripley, 1987) is used when there are more than 250 reasonably
probable values: this gives results incompatible with those from R <
2.2.0, and there will be a warning the first time this happens in a
session.
If replace is false, these probabilities are applied
sequentially, that is the probability of choosing the next item is
proportional to the weights amongst the remaining items. The number
of nonzero weights must be at least size in this case.
sample.int is a bare interface in which both n and
size must be supplied as integers.
For sample a vector of length size with elements
drawn from either x or from 1:x.
For sample.int, an integer vector of length size with
elements from 1:n,
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
Ripley, B. D. (1987) Stochastic Simulation. Wiley.
Package sampling for other methods of weighted sampling without replacement.
x <- 1:12
# a random permutation
sample(x)
# bootstrap sampling -- only if length(x) > 1 !
sample(x,replace=TRUE)
# 100 Bernoulli trials
sample(c(0,1), 100, replace = TRUE)
## More careful bootstrapping -- Consider this when using sample()
## programmatically (i.e., in your function or simulation)!
# sample()'s surprise -- example
x <- 1:10
sample(x[x > 8]) # length 2
sample(x[x > 9]) # oops -- length 10!
try(sample(x[x > 10]))# error!
## This is safer, but only for sampling without replacement
resample <- function(x, size, ...)
if(length(x) <= 1) { if(!missing(size) && size == 0) x[FALSE] else x
} else sample(x, size, ...)
resample(x[x > 8])# length 2
resample(x[x > 9])# length 1
resample(x[x > 10])# length 0