R: Random Samples and Permutations

sample {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)

    sample.int(n, size = n, replace = FALSE, prob = NULL)

### Arguments

`x` | Either a vector of one or more elements from which to choose, or a positive integer. See ‘Details.’
`n` | a positive number, the number of items to choose from. See ‘Details.’
`size` | a 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 via `sample` takes place from `1:x`. Note that this convenience feature may lead to undesired behaviour when `x` is of varying length in calls such as `sample(x)`. See the examples.

Otherwise `x` can be any R object for which `length` and subsetting by integers make sense: S3 or S4 methods for these operations will be dispatched as appropriate.

For `sample` the default for `size` is the number of items inferred from the first argument, so that `sample(x)` generates a random permutation of the elements of `x` (or `1:x`).

It is allowed to ask for `size = 0` samples with `n = 0` or a length-zero `x`, but otherwise `n > 0` or positive `length(x)` is required.

Non-integer positive numerical values of `n` or `x` will be truncated to the next smallest integer, which has to be no larger than `.Machine$integer.max`.

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 non-negative and not all zero. If `replace` is true, Walker's alias method (Ripley, 1987) is used when there are more than 200 reasonably probable values: this gives results incompatible with those from R < 2.2.0.

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.

As from R 3.0.0, `n` can be larger than the largest integer of type `integer`, up to the largest representable integer in type `double`. Only uniform sampling is supported. Two random numbers are used to ensure uniform sampling of large integers.

### Value

For `sample` a vector of length `size` with elements drawn from either `x` or from the integers `1:x`.

For `sample.int`, an integer vector of length `size` with elements from `1:n`, or a double vector if n >= 2^31.

### 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

`RNG` about random number generation.

CRAN package sampling for other methods of weighted sampling without replacement.

### Examples

    x <- 1:12
    # a random permutation
    sample(x)
    # bootstrap resampling -- 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!
        sample(x[x > 10]) # length 0

    resample <- function(x, ...) x[sample.int(length(x), ...)]
    resample(x[x >  8]) # length 2
    resample(x[x >  9]) # length 1
    resample(x[x > 10]) # length 0

    ## R 3.x.y only
    sample.int(1e10, 12, replace = TRUE)
    sample.int(1e10, 12) # not that there is much chance of duplicates

[Package base version 3.1.2 Index]