bootstrap {arkhe} | R Documentation |

## Bootstrap Estimation

### Description

Samples randomly from the elements of `object`

with replacement.

### Usage

```
bootstrap(object, ...)
## S4 method for signature 'numeric'
bootstrap(object, do, n, ..., f = NULL)
```

### Arguments

`object` |
A |

`...` |
Extra arguments to be passed to |

`do` |
A |

`n` |
A non-negative |

`f` |
A |

### Value

If `f`

is `NULL`

(the default), `bootstrap()`

returns a named `numeric`

vector with the following elements:

`original`

The observed value of

`do`

applied to`object`

.`mean`

The bootstrap estimate of mean of

`do`

.`bias`

The bootstrap estimate of bias of

`do`

.`error`

he bootstrap estimate of standard error of

`do`

.

If `f`

is a `function`

, `bootstrap()`

returns the result of `f`

applied to
the `n`

values of `do`

.

### Author(s)

N. Frerebeau

### See Also

Other resampling methods:
`jackknife()`

### Examples

```
x <- rnorm(20)
## Bootstrap
bootstrap(x, do = mean, n = 100)
## Estimate the 25th and 95th percentiles
quant <- function(x) { quantile(x, probs = c(0.25, 0.75)) }
bootstrap(x, n = 100, do = mean, f = quant)
## Get the n bootstrap values
bootstrap(x, n = 100, do = mean, f = function(x) { x })
## Jackknife
jackknife(x, do = mean) # Sample mean
## Get the leave-one-out values instead of summary
jackknife(x, do = mean, f = function(x) { x })
```

[Package

*arkhe*version 1.7.0 Index]