Bootstrap Resampling Arrays
This function takes a bootstrap object calculated by one of the
functions boot, censboot, or tilt.boot and
returns the frequency (or index) array for the bootstrap
resamples.
boot.array(boot.out, indices)
boot.out |
An object of class |
indices |
A logical argument which specifies whether to return the frequency
array or the raw index array. The default is |
The process by which the original index array was generated is
repeated with the same value of .Random.seed. If the frequency
array is required then freq.array is called to convert the
index array to a frequency array.
A resampling array can only be returned when such a concept makes
sense. In particular it cannot be found for any parametric or
model-based resampling schemes. Hence for objects generated by
censboot the only resampling scheme for which such an array can
be found is ordinary case resampling. Similarly if boot.out$sim
is "parametric" in the case of boot or "model" in
the case of tsboot the array cannot be found. Note also that
for post-blackened bootstraps from tsboot the indices found
will relate to those prior to any post-blackening and so will not be
useful.
Frequency arrays are used in many post-bootstrap calculations such as the jackknife-after-bootstrap and finding importance sampling weights. They are also used to find empirical influence values through the regression method.
A matrix with boot.out$R rows and n columns where
n is the number of observations in boot.out$data. If
indices is FALSE then this will give the frequency of
each of the original observations in each bootstrap resample. If
indices is TRUE it will give the indices of the
bootstrap resamples in the order in which they would have been passed
to the statistic.
This function temporarily resets .Random.seed to the value in
boot.out$seed and then returns it to its original value at the
end of the function.
# A frequency array for a nonparametric bootstrap city.boot <- boot(city, corr, R = 40, stype = "w") boot.array(city.boot) perm.cor <- function(d,i) cor(d$x,d$u[i]) city.perm <- boot(city, perm.cor, R = 40, sim = "permutation") boot.array(city.perm, indices = TRUE)
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