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added more demos to bootstrp and updated MATLAB toolbox
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‎DESCRIPTION

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name: statistics-resampling
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version: 5.5.9
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date: 2024-04-21
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date: 2024-04-24
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author: Andrew Penn <andy.c.penn@gmail.com>
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maintainer: Andrew Penn <andy.c.penn@gmail.com>
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title: A statistics package with a variety of resampling tools

‎inst/bootstrp.m

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w = value;
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case {'options', 'option'}
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paropt = value;
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case 'match'
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case {'match', 'matched', 'matching'}
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match = value;
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case 'seed'
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seed = value;
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%! 0 33 28 34 4 32 24 47 41 24 26 30 41]';
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%!
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%! % Compute 50 bootstrap statistics for the mean and calculate the bootstrap
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%! % standard error
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%! bootstat = bootstrp (50, @mean, data)
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%! % standard error of the mean
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%! bootstat = bootstrp (50, @mean, data, 'seed', 1);
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%! % Or equivalently
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%! bootstat = bootstrp (50, @mean, data, 'seed', 1, 'loo', false);
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%! std (bootstat)
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%!demo
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%!
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%! % Input univariate dataset
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%! data = [48 36 20 29 42 42 20 42 22 41 45 14 6 ...
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%! 0 33 28 34 4 32 24 47 41 24 26 30 41]';
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%!
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%! % Compute 50 bootknife statistics for the mean and calculate the unbiased
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%! % bootstrap standard error of the mean
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%! bootstat = bootstrp (50, @mean, data, 'seed', 1, 'loo', true);
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%! std (bootstat)
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%!demo
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%!
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%! % Input univariate dataset
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%! data = [48 36 20 29 42 42 20 42 22 41 45 14 6 ...
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%! 0 33 28 34 4 32 24 47 41 24 26 30 41]';
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%! % Split data into consecutive blocks of two data observations per cell
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%! data_blocks = mat2cell (data, 2 * (ones (13, 1)), 1);
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%!
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%! % Compute 50 bootknife statistics for the mean and calculate the unbiased
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%! % bootstrap standard error of the mean
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%! bootstat = bootstrp (50, @(x) mean (cell2mat (x)), data_blocks, 'seed', 1, ...
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%! 'loo', true);
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%! std (bootstat)
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%!demo
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%!
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%! % Input univariate dataset
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%! data = [48 36 20 29 42 42 20 42 22 41 45 14 6 ...
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%! 0 33 28 34 4 32 24 47 41 24 26 30 41]';
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%!
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%! % Compute 50 bootknife statistics for the variance and calculate the
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%! % unbiased standard error of the variance
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%! bootstat = bootstrp (50, {@var, 1}, data, 'loo', true);
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%! std (bootstat)
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%!demo
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%!
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%! % Input two-sample dataset
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%! X = [212 435 339 251 404 510 377 335 410 335 ...
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%! 415 356 339 188 256 296 249 303 266 300]';
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%! Y = [247 461 526 302 636 593 393 409 488 381 ...
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%! 474 329 555 282 423 323 256 431 437 240]';
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%!
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%! % Compute 50 bootknife statistics for the mean difference between X and Y
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%! % and calculate the unbiased bootstrap standard error of the mean difference
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%! bootstat = bootstrp (50, @(x, y) mean (x - y), X, Y, 'loo', true);
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%! % Or equivalently
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%! bootstat = bootstrp (50, @(x, y) mean (x - y), X, Y, 'loo', true, ...
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%! 'match', true);
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%! std (bootstat)
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%!demo
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%!
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%! % Input two-sample dataset
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%! X = [212 435 339 251 404 510 377 335 410 335 ...
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%! 415 356 339 188 256 296 249 303 266 300]';
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%! Y = [247 461 526 302 636 593 393 409 488 381 ...
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%! 474 329 555 282 423 323 256 431 437 240]';
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%!
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%! % Compute 50 bootknife statistics for the difference in mean between
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%! % between independent samples X and Y and calculate the unbiased bootstrap
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%! % standard error of the difference in mean
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%! bootstat = bootstrp (50, @(x, y) mean (x) - mean(y), X, Y, 'loo', true, ...
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%! 'match', false);
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%! std (bootstat)
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%!demo
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%!
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%! % Input bivariate dataset
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%! X = [212 435 339 251 404 510 377 335 410 335 ...
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%! 415 356 339 188 256 296 249 303 266 300]';
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%! Y = [247 461 526 302 636 593 393 409 488 381 ...
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%! 474 329 555 282 423 323 256 431 437 240]';
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%!
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%! % Compute 50 bootstrap statistics for the correlation coefficient and
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%! % calculate the bootstrap standard error of the correlation coefficient
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%! bootstat = bootstrp (50, @cor, X, Y);
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%! std (bootstat)
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%!demo
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%!
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%! % Input bivariate dataset
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%! X = [212 435 339 251 404 510 377 335 410 335 ...
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%! 415 356 339 188 256 296 249 303 266 300]';
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%! Y = [247 461 526 302 636 593 393 409 488 381 ...
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%! 474 329 555 282 423 323 256 431 437 240]';
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%!
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%! % Compute 50 bootstrap statistics for the coefficient of determination and
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%! % calculate the bootstrap standard error of the coefficient of determination
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%! bootstat = bootstrp (50, {@cor,'squared'}, X, Y);
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%! std (bootstat)
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%!demo
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%!
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%! % Input bivariate dataset
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%! X = [212 435 339 251 404 510 377 335 410 335 ...
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%! 415 356 339 188 256 296 249 303 266 300]';
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%! Y = [247 461 526 302 636 593 393 409 488 381 ...
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%! 474 329 555 282 423 323 256 431 437 240]';
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%!
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%! % Compute 50 bootstrap statistics for the slope and intercept of a linear
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%! % regression and calculate there bootstrap standard errors
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%! bootstat = bootstrp (50, @mldivide, cat (2, ones (20, 1), X), Y);
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%! std (bootstat)
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%!test

‎matlab/statistics-resampling.mltbx

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‎matlab/statistics-resampling.prj

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<deployment-project plugin="plugin.toolbox" plugin-version="1.0">
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<configuration build-checksum="1816228546" file="Y:\Documents\GitHub\statistics-resampling\matlab\statistics-resampling.prj" location="Y:\Documents\GitHub\statistics-resampling\matlab" name="statistics-resampling" target="target.toolbox" target-name="Package Toolbox">
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<configuration build-checksum="2178968502" file="Y:\Documents\GitHub\statistics-resampling\matlab\statistics-resampling.prj" location="Y:\Documents\GitHub\statistics-resampling\matlab" name="statistics-resampling" target="target.toolbox" target-name="Package Toolbox">
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<param.appname>statistics-resampling</param.appname>
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<param.authnamewatermark>Andrew Penn</param.authnamewatermark>
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<param.email>andy.c.penn@gmail.com</param.email>

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