bootstrap {stablelearner} | 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
.
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)