sample package:base R Documentation

Random Samples and Permutations

**Description:**

'sample' takes a sample of the specified size from the elements of 'x' using either with or without replacement.

**Usage:**

sample(x, size, replace = FALSE, prob = NULL)

**Arguments:**

x: Either a (numeric, complex, character or logical) vector of more than one element from which to choose, or a positive integer.

size: non-negative 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.

**Details:**

If 'x' has length 1, is numeric (in the sense of 'is.numeric') and 'x >= 1', sampling 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.

**References:**

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.

**See Also:**

Package 'sampling' for other methods of weighted sampling without replacement.

**Examples:**

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