Balanced bootstrap resampling. -- Function File: BOOTSTAT = bootstrp (NBOOT, BOOTFUN, D) -- Function File: BOOTSTAT = bootstrp (NBOOT, BOOTFUN, D1, ..., DN) -- Function File: BOOTSTAT = bootstrp (..., 'seed', SEED) -- Function File: BOOTSTAT = bootstrp (..., 'Options', PAROPT) -- Function File: [BOOTSTAT, BOOTSAM] = bootstrp (...) BOOTSTAT = bootstrp (NBOOT, BOOTFUN, D) draws NBOOT bootstrap resamples from the data D and returns the statistic computed by BOOTFUN in BOOTSTAT [1]. bootstrp resamples from the rows of a data sample D (column vector or a matrix). BOOTFUN is a function handle (e.g. specified with @), or a string indicating the function name. The third input argument is data (column vector or a matrix), that is used to create inputs for BOOTFUN. The resampling method used throughout is balanced bootstrap resampling [2-3]. BOOTSTAT = bootstrp (NBOOT, BOOTFUN, D1,...,DN) is as above except that the third and subsequent numeric input arguments are data vectors that are used to create inputs for BOOTFUN. BOOTSTAT = bootstrp (..., 'seed', SEED) initialises the Mersenne Twister random number generator using an integer SEED value so that bootci results are reproducible. BOOTSTAT = bootstrp (..., 'Options', PAROPT) specifies options that govern if and how to perform bootstrap iterations using multiple processors (if the Parallel Computing Toolbox or Octave Parallel package is available). This argument is a structure with the following recognised fields: o 'UseParallel': If true, use parallel processes to accelerate bootstrap computations on multicore machines. Default is false for serial computation. In MATLAB, the default is true if a parallel pool has already been started. o 'nproc': nproc sets the number of parallel processes [BOOTSTAT, BOOTSAM] = bootstrp (...) also returns BOOTSAM, a matrix of indices from the bootstrap. Each column in BOOTSAM corresponds to one bootstrap sample and contains the row indices of the values drawn from the nonscalar data argument to create that sample. Bibliography: [1] Efron, and Tibshirani (1993) An Introduction to the Bootstrap. New York, NY: Chapman & Hall [2] Davison et al. (1986) Efficient Bootstrap Simulation. Biometrika, 73: 555-66 [3] Booth, Hall and Wood (1993) Balanced Importance Resampling for the Bootstrap. The Annals of Statistics. 21(1):286-298 bootstrp (version 2023.06.20) Author: Andrew Charles Penn https://www.researchgate.net/profile/Andrew_Penn/ Copyright 2019 Andrew Charles Penn This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/
The following code
% Input univariate dataset data = [48 36 20 29 42 42 20 42 22 41 45 14 6 ... 0 33 28 34 4 32 24 47 41 24 26 30 41]'; % Compute 50 bootstrap statistics for the mean and calculate the bootstrap % standard arror bootstat = bootstrp (50, @mean, data) std (bootstat)
Produces the following output
bootstat = 30.385 26.577 32.962 26.885 28.962 27.385 29.538 28.615 36.269 29.192 36.192 35.346 30.923 31.423 30.231 25.692 28.577 27.038 26.269 31.423 30.154 29.654 28.577 32.154 28.385 32.615 24.846 32.269 25.577 29.615 26.923 27.038 26.731 33.923 31.962 26.538 26.846 29.769 27 26.731 29.231 30.885 31.077 30 29.115 31.769 32.423 30.692 29.231 31.077 ans = 2.7164
Package: statistics-resampling