| 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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