resample: Resampling Methods

Description

resample() simulate observations from a multinomial distribution.
bootstrap() generate bootstrap estimations of a statistic.
jackknife() generate jackknife estimations of a statistic.

Usage

resample(object, ...)
bootstrap(object, ...)
jackknife(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)

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.

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.

Author

N. Frerebeau

Examples

Run this code

## Sample observations from a multinomial distribution
x <- sample(1:100, 50, TRUE)
resample(x, do = median, n = 100)

## 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)

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