R: Generates a weighted sample (with replacement) of ranks

rankSample {nimble} | R Documentation

## Generates a weighted sample (with replacement) of ranks

### Description

Takes a set of non-negative `weights` (do not need to sum to 1) and returns a sample with `size` elements of the integers `1:length(weights)`, where the probability of being sampled is proportional to the value of `weights`. An important note is that the output vector will be sorted in ascending order. Also, right now it works slightly odd syntax (see example below). Later releases of NIMBLE will contain more natural syntax.

### Usage

    rankSample(weights, size, output, silent = FALSE)

### Arguments

`weights` | A vector of numeric weights. Does not need to sum to 1, but must be non-negative
`size` | Size of sample
`output` | An R object into which the values will be placed. See example below for proper use
`silent` | Logical indicating whether to suppress logging information

### Details

If invalid weights provided (i.e. negative weights or weights sum to 1), sets output = rep(1, size) and prints warning. `rankSample` can be used inside nimble functions.

`rankSample` first samples from the joint distribution `size` uniform(0,1) distributions by conditionally sampling from the rank statistics. This leads to a sorted sample of uniform(0,1)'s. Then, a cdf vector is constructed from weights. Because the sample of uniforms is sorted, `rankSample` walks down the cdf in linear time and fills out the sample.

### Author(s)

Clifford Anderson-Bergman

### Examples

    set.seed(1)
    sampInts = NA	#sampled integers will be placed in sampInts
    rankSample(weights = c(1, 1, 2), size = 10, sampInts)
    sampInts
    # [1] 1 1 2 2 2 2 2 3 3 3
    rankSample(weights = c(1, 1, 2), size = 10000, sampInts)
    table(sampInts)
    #sampInts
    #   1    2    3
    #2434 2492 5074

    #Used in a nimbleFunction
    sampGen <- nimbleFunction(setup = function(){
    	x = 1:2
    },
    run = function(weights = double(1), k = integer() ){
    	rankSample(weights, k, x)
    	returnType(integer(1))
    	return(x)
    })
    rSamp <- sampGen()
    rSamp$run(1:4, 5)
    #[1] 3 3 4 4 4

[Package nimble version 0.10.0 Index]