bootstrap | R Documentation |

Sampler objects that provide objects with functionality used by
`stabletree`

to generate resampled datasets.

bootstrap(B = 500, v = 1) subsampling(B = 500, v = 0.632) samplesplitting(k = 5) jackknife(d = 1, maxrep = 5000) splithalf(B = 500)

`B` |
An integer value specifying the number of resampled datasets. |

`k` |
An integer value specifying the number of folds in sample-splitting. |

`d` |
An integer value specifying the number of observations left out in jackknife. |

`maxrep` |
An integer value specifying the maximum number of resampled datasets allowed, when using jackknife. |

`v` |
A numeric value between 0 and 1 specifying the fraction of observations in each subsample. |

The sampler functions provide objects that include functionality to generate
resampled datasets used by `stabletree`

.

The `bootstrap`

function provides an object that can be used to generate
`B`

bootstrap samples by sampling from `n`

observations with
replacement.

The `subsampling`

function provides an object that can be used to
generate `B`

subsamples by sampling from `floor(v*n)`

observations without replacement.

The `samplesplitting`

function provides an object that can be used to
generate `k`

-folds from `n`

observations.

The `jackknife`

function provides an object that can be used to generate
all datasets necessary to perform leave-`k`

-out jackknife sampling from
`n`

observations. The number of datasets is limited by `maxrep`

to
prevent unintended CPU or memory overload by accidently choosing too large
values for `k`

.

The `splithalf`

function provides an object that can be used to
generate `B`

subsamples by sampling from `floor(0.5*n)`

observations without replacement. When used to implement the "splithalf"
resampling strategy for measuring the stability of a result via the
`stability`

function, the matrix containing the complement
learning samples is generated automatically by `stability`

.

`stabletree`

, `stability`

set.seed(0) ## bootstrap sampler s <- bootstrap(3) s$sampler(10) ## subsampling s <- subsampling(3, v = 0.6) s$sampler(10) ## 5-fold sample-splitting s <- samplesplitting(5) s$sampler(10) ## jackknife s <- jackknife(d = 1) s$sampler(10) ## splithaf s <- splithalf(3) s$sampler(10)

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