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Resampling Methods

Source: R/AllGenerics.R, R/index_diversity.R, R/statistics.R
resample.Rd

  • resample() simulate observations from a multinomial distribution.

  • bootstrap() generate bootstrap estimations of a statistic.

  • jackknife() generate jackknife estimations of a statistic.

Usage

resample(object, ...)

# S4 method for DiversityIndex
bootstrap(object, n = 1000, f = NULL)

# S4 method for DiversityIndex
jackknife(object)

# S4 method for numeric
resample(object, do, n, size = sum(object), ..., f = NULL)

Arguments

object

A numeric vector of count data (absolute frequencies).

...

Extra arguments passed to do.

n

A non-negative integer specifying the number of bootstrap replications.

f

A function that takes a single numeric vector (the result of do) as argument.

do

A function that takes object as an argument and returns a single numeric value.

size

A non-negative integer specifying the sample size.

Value

If f is NULL, resample() returns the n values of do. Else, returns the result of f applied to the n values of do.

If f is NULL, bootstrap() and jackknife() return a data.frame

with the following elements (else, returns the result of f applied to the n values of do) :

original

The observed value of do applied to object.

mean

The bootstrap/jackknife estimate of mean of do.

bias

The bootstrap/jackknife estimate of bias of do.

error

The boostrap/jackknife estimate of standard error of do.

See also

stats::rmultinom()

Author

N. Frerebeau

Examples

## Sample observations from a multinomial distribution
x <- sample(1:100, 50, TRUE)
resample(x, do = median, n = 100)
#>   [1] 45.0 46.5 45.5 50.0 49.0 45.0 46.5 49.0 49.5 52.0 49.5 49.5 46.0 40.5 45.5
#>  [16] 47.5 47.0 45.5 50.0 44.0 46.5 45.5 46.5 43.5 45.5 46.0 47.5 47.0 49.0 48.5
#>  [31] 50.5 46.5 51.0 47.5 49.0 46.5 47.5 48.5 46.5 49.5 44.5 46.5 46.5 46.0 47.0
#>  [46] 45.5 46.5 52.5 45.0 47.0 49.0 47.5 45.0 46.0 47.0 46.0 50.0 49.5 47.0 43.0
#>  [61] 46.0 49.0 44.0 48.5 50.0 48.5 48.0 47.5 46.5 47.5 49.0 45.5 47.0 45.5 47.5
#>  [76] 43.0 47.5 47.0 49.5 46.5 46.5 47.5 47.5 48.5 46.0 44.0 47.0 47.0 45.5 45.0
#>  [91] 49.0 47.5 46.0 51.0 52.0 45.5 43.0 47.5 45.5 48.0

## Estimate the 25th, 50th and 95th percentiles
quant <- function(x) { quantile(x, probs = c(0.25, 0.50, 0.75)) }
resample(x, n = 100, do = median, f = quant)
#>  25%  50%  75% 
#> 45.5 47.0 48.5