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test_script.m
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% This script checks for operational errors in the functions of this package
lasterror ('reset');
try
% boot
boot (3, 20);
boot (3, 20, false, 1);
boot (3, 20, true, 1);
boot (3, 20, [], 1);
boot (3, 20, true, 1, [30,30,0]);
% bootknife
% bootknife:test:1
y = randn (20,1);
strata = [1;1;1;1;1;1;1;1;1;1;2;2;2;2;2;3;3;3;3;3];
stats = bootknife (y, 2000, @mean);
stats = bootknife (y, 2000, 'mean');
stats = bootknife (y, 2000, {@var,1});
stats = bootknife (y, 2000, {'var',1});
stats = bootknife (y, 2000, @mean, [], strata);
stats = bootknife (y, 2000, {'var',1}, [], strata);
stats = bootknife (y, 2000, {@var,1}, [], strata, 2);
stats = bootknife (y, 2000, @mean, .1, strata, 2);
stats = bootknife (y, 2000, @mean, [.05,.95], strata, 2);
stats = bootknife (y, [2000,200], @mean, .1, strata, 2);
stats = bootknife (y, [2000,200], @mean, [.05,.95], strata, 2);
stats = bootknife (y(1:5), 2000, @mean, .1);
stats = bootknife (y(1:5), 2000, @mean, [.05,.95]);
stats = bootknife (y(1:5), [2000,200], @mean, .1);
stats = bootknife (y(1:5), [2000,200], @mean, [.05,.95]);
% bootknife:test:2
Y = randn (20);
strata = [1;1;1;1;1;1;1;1;1;1;2;2;2;2;2;3;3;3;3;3];
stats = bootknife (Y, 2000, @mean);
stats = bootknife (Y, 2000, 'mean');
stats = bootknife (Y, 2000, {@var, 1});
stats = bootknife (Y, 2000, {'var',1});
stats = bootknife (Y, 2000, @mean, [], strata);
stats = bootknife (Y, 2000, {'var',1}, [], strata);
stats = bootknife (Y, 2000, {@var,1}, [], strata, 2);
stats = bootknife (Y, 2000, @mean, .1, strata, 2);
stats = bootknife (Y, 2000, @mean, [.05,.95], strata, 2);
stats = bootknife (Y, [2000,200], @mean, .1, strata, 2);
stats = bootknife (Y, [2000,200], @mean, [.05,.95], strata, 2);
stats = bootknife (Y(1:5,:), 2000, @mean, .1);
stats = bootknife (Y(1:5,:), 2000, @mean, [.05,.95]);
stats = bootknife (Y(1:5,:), [2000,200], @mean, .1);
stats = bootknife (Y(1:5,:), [2000,200], @mean, [.05,.95]);
stats = bootknife (Y, 2000, @(Y) mean(Y(:),1)); % Cluster/block resampling
% Y(1,end) = NaN; % Unequal clustersize
%stats = bootknife (Y, 2000, @(Y) mean(Y(:),1,'omitnan'));
% bootknife:test:3
y = randn (20,1); x = randn (20,1); X = [ones(20,1), x];
stats = bootknife ({x,y}, 2000, @cor);
stats = bootknife ({x,y}, 2000, @cor, [], strata);
stats = bootknife ({X,y}, 2000, @mldivide);
stats = bootknife ({X,y}, 2000, @mldivide, [], strata);
stats = bootknife ({X,y}, 2000, @mldivide, [], strata, 2);
stats = bootknife ({X,y}, 2000, @mldivide, [.05,.95], strata);
% bootclust
% bootclust:test:1
y = randn (20,1);
clustid = [1;1;1;1;1;1;1;1;1;1;2;2;2;2;2;3;3;3;3;3];
stats = bootclust (y, 1999, @mean);
stats = bootclust (y, 1999, 'mean');
stats = bootclust (y, 1999, {@var,1});
stats = bootclust (y, 1999, {'var',1});
stats = bootclust (y, 1999, @mean, [], 4);
stats = bootclust (y, 1999, @mean, [], clustid);
stats = bootclust (y, 1999, {'var',1}, [], clustid);
stats = bootclust (y, 1999, {'var',1}, [], clustid, true);
stats = bootclust (y, 1999, {@var,1}, [], clustid, true, 1);
stats = bootclust (y, 1999, @mean, .1, clustid, true);
stats = bootclust (y, 1999, @mean, .1, clustid, true, 1);
stats = bootclust (y, 1999, @mean, [.05,.95], clustid, true);
stats = bootclust (y, 1999, @mean, [.05,.95], clustid, true, 1);
stats = bootclust (y(1:5), 1999, @mean, .1);
stats = bootclust (y(1:5), 1999, @mean, [.05,.95]);
% bootclust:test:2
Y = randn (20);
clustid = [1;1;1;1;1;1;1;1;1;1;2;2;2;2;2;3;3;3;3;3];
stats = bootclust (Y, 1999, @mean);
stats = bootclust (Y, 1999, 'mean');
stats = bootclust (Y, 1999, {@var, 1});
stats = bootclust (Y, 1999, {'var',1});
stats = bootclust (Y, 1999, @mean, [], clustid);
stats = bootclust (Y, 1999, {'var',1}, [], clustid);
stats = bootclust (Y, 1999, {@var,1}, [], clustid, true);
stats = bootclust (Y, 1999, {@var,1}, [], clustid, true, 1);
stats = bootclust (Y, 1999, @mean, .1, clustid, true);
stats = bootclust (Y, 1999, @mean, .1, clustid, true, 1);
stats = bootclust (Y, 1999, @mean, [.05,.95], clustid, true);
stats = bootclust (Y, 1999, @mean, [.05,.95], clustid, true, 1);
stats = bootclust (Y(1:5,:), 1999, @mean, .1);
stats = bootclust (Y(1:5,:), 1999, @mean, [.05,.95]);
% bootclust:test:3
y = randn (20,1); x = randn (20,1); X = [ones(20,1), x];
stats = bootclust ({x,y}, 1999, @cor);
stats = bootclust ({x,y}, 1999, @cor, [], clustid);
stats = bootclust ({x,y}, 1999, @mldivide);
stats = bootclust ({X,y}, 1999, @mldivide);
stats = bootclust ({X,y}, 1999, @mldivide, [], clustid);
stats = bootclust ({X,y}, 1999, @mldivide, [], clustid, true);
stats = bootclust ({X,y}, 1999, @mldivide, [], clustid, true, 1);
stats = bootclust ({X,y}, 1999, @mldivide, [.05,.95], clustid);
% bootci
% bootci:test:1
y = randn (20, 1);
bootci (1999, 'mean', y);
bootci (1999, @mean, y);
bootci (1999, @mean, y, 'alpha', 0.1);
bootci (1999, {'mean', y}, 'alpha', 0.1);
bootci (1999, {'mean', y}, 'strata', []);
bootci (1999, {'mean', y}, 'loo', false);
bootci (1999, {'mean', y}, 'loo', true);
bootci (1999, {@mean, y}, 'alpha', 0.1);
bootci (1999, {@mean, y}, 'alpha', 0.1, 'seed', 1);
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'norm');
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'per');
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'basic');
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'bca');
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'stud');
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'stud', 'nbootstd', 100);
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'cal');
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'cal', 'nbootcal', 199);
g = reshape (repmat ((1:5), 4, 1), 20, []);
bootci (1999, {@mean, y}, 'alpha', 0.1, 'strata', []);
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'norm', 'strata', g);
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'per', 'strata', g);
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'basic', 'strata', g);
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'bca', 'strata', g);
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'stud', 'strata', g);
bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'cal', 'strata', g);
% bootci:test:2
Y = randn (20);
bootci (1999, 'mean', Y);
bootci (1999, @mean, Y);
bootci (1999, @mean, Y, 'alpha', 0.1);
bootci (1999, {'mean', Y}, 'alpha', 0.1);
bootci (1999, {@mean, Y}, 'alpha', 0.1);
bootci (1999, {@mean, Y}, 'alpha', 0.1, 'strata', g);
bootci (1999, {@mean, Y}, 'alpha', 0.1, 'seed', 1);
bootci (1999, {@mean, Y}, 'alpha', 0.1, 'type', 'norm');
bootci (1999, {@mean, Y}, 'alpha', 0.1, 'type', 'per');
bootci (1999, {@mean, Y}, 'alpha', 0.1, 'type', 'basic');
bootci (1999, {@mean, Y}, 'alpha', 0.1, 'type', 'bca');
bootci (1999, {@mean, Y}, 'alpha', 0.1, 'type', 'stud');
bootci (1999, {@mean, Y}, 'alpha', 0.1, 'type', 'cal');
% bootci:test:3
y = randn (20,1); x = randn (20,1); X = [ones(20,1),x];
bootci (1999, @cor, x, y);
bootci (1999, {@cor, x, y}, 'strata', g);
bootci (1999, @mldivide, X, y);
bootci (1999, @mldivide, X, y, 'alpha', 0.1);
bootci (1999, {@mldivide, X, y}, 'alpha', 0.1);
bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'norm');
bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'per');
bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'basic');
bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'bca');
bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'stud');
bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'cal');
% bootstrp:test:1
X = [212 435 339 251 404 510 377 335 410 335 ...
415 356 339 188 256 296 249 303 266 300]';
Y = [247 461 526 302 636 593 393 409 488 381 ...
474 329 555 282 423 323 256 431 437 240]';
Z = cat (1, X, Y);
bootstrp (50, @mean, X);
bootstat = bootstrp (50, @mean, X);
[bootstat, bootsam] = bootstrp (50, @mean, X);
[bootstat, bootsam] = bootstrp (50, [], X);
bootstrp (50, @(x) mean (cell2mat (x)), num2cell (X, 2));
bootstrp (50, @(x, y) mean (x) - mean (y), X, Y);
bootstrp (50, @(x, y) mean (x - y), X, Y);
bootstrp (50, @(x, y) mean (x - y), X, Y, 'match', true);
bootstrp (50, @(x, y) mean (x) - mean (y), X, Y, 'match', false);
bootstrp (50, @(x, z) mean (x) - mean (z), X, Z, 'match', false);
bootstrp (50, @var, X);
bootstrp (50, {@var, 1}, X);
bootstrp (50, @cor, X, Y);
bootstrp (50, {@cor,'squared'}, X, Y);
bootstrp (50, @(x, y) cor (cell2mat (x), cell2mat (y)), num2cell (X, 2), ...
num2cell (Y, 2));
bootstrp (50, @mldivide, X, Y);
bootstrp (50, @mldivide, cat (2, ones (20, 1), X), Y);
bootstrp (50, @(x, y) mldivide (x, cell2mat (y)), ...
cat (2, ones (20, 1), X), num2cell (Y, 2));
bootstrp (50, @mean, X, 'seed', 1);
bootstrp (50, @mean, X, 'loo', false);
bootstrp (50, @mean, X, 'Weights', rand (20, 1));
bootstrp (50, @mean, X, 'seed', 1, 'loo', false, 'Weights', rand (20, 1));
% boot1way
% boot1way:test:1
y = [111.39 110.21 89.21 76.64 95.35 90.97 62.78;
112.93 60.36 92.29 59.54 98.93 97.03 79.65;
85.24 109.63 64.93 75.69 95.28 57.41 75.83;
111.96 103.40 75.49 76.69 77.95 93.32 78.70];
g = [1 2 3 4 5 6 7;
1 2 3 4 5 6 7;
1 2 3 4 5 6 7;
1 2 3 4 5 6 7];
p = boot1way (y(:),g(:),'ref',1,'nboot',[999,0],'DisplayOpt',false);
p = boot1way (y(:),g(:),'nboot',[999,0],'DisplayOpt',false);
% boot1way:test:2
y = [54 43
23 34
45 65
54 77
45 46
NaN 65];
g = {'male' 'female'
'male' 'female'
'male' 'female'
'male' 'female'
'male' 'female'
'male' 'female'};
p = boot1way (y(:),g(:),'ref','male','nboot',[999,0],'DisplayOpt',false);
p = boot1way (y(:),g(:),'nboot',[999,0],'DisplayOpt',false);
% boot1way:test:3
y = [54 87 45
23 98 39
45 64 51
54 77 49
45 89 50
47 NaN 55];
g = [ 1 2 3
1 2 3
1 2 3
1 2 3
1 2 3
1 2 3];
p = boot1way (y(:),g(:),'nboot',[999,0],'DisplayOpt',false);
p = boot1way (y(:),g(:),'bootfun',@(y)std(y,1),'DisplayOpt',false);
p = boot1way (y(:),g(:),'bootfun',{@std,1},'DisplayOpt',false);
% boot1way:test:4
Y = randn (20, 2); g = [zeros(10, 1); ones(10, 1)];
func = @(M) cor (M(:,1), M(:,2));
p = boot1way (Y, g, 'bootfun', func, 'DisplayOpt', false);
% bootmode
% bootmode:test:1
x=[0.060;0.064;0.064;0.065;0.066;0.068;0.069;0.069;0.069;0.069;0.069;0.069;0.069;0.070;0.070;0.070;
0.070;0.070;0.070;0.070;0.070;0.070;0.070;0.070;0.070;0.070;0.070;0.070;0.070;0.070;0.070;0.070;
0.070;0.070;0.070;0.070;0.070;0.070;0.070;0.071;0.071;0.071;0.071;0.071;0.071;0.071;0.071;0.071;
0.071;0.071;0.071;0.071;0.071;0.071;0.071;0.071;0.071;0.071;0.071;0.072;0.072;0.072;0.072;0.072;
0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;
0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.072;0.073;0.073;0.073;0.073;0.073;
0.073;0.073;0.073;0.073;0.073;0.073;0.074;0.074;0.074;0.074;0.074;0.074;0.074;0.074;0.074;0.074;
0.075;0.075;0.075;0.075;0.075;0.075;0.075;0.075;0.075;0.075;0.075;0.075;0.075;0.075;0.075;0.075;
0.075;0.075;0.075;0.075;0.076;0.076;0.076;0.076;0.076;0.076;0.076;0.076;0.076;0.076;0.076;0.076;
0.076;0.076;0.076;0.076;0.076;0.076;0.077;0.077;0.077;0.077;0.077;0.077;0.077;0.077;0.077;0.077;
0.077;0.078;0.078;0.078;0.078;0.078;0.078;0.078;0.078;0.078;0.078;0.078;0.078;0.078;0.078;0.078;
0.078;0.078;0.078;0.078;0.078;0.078;0.078;0.078;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;
0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;
0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;0.079;
0.079;0.079;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;
0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.080;
0.080;0.080;0.080;0.080;0.080;0.080;0.080;0.081;0.081;0.081;0.081;0.081;0.081;0.081;0.081;0.081;
0.081;0.081;0.081;0.081;0.081;0.081;0.082;0.082;0.082;0.082;0.082;0.082;0.082;0.082;0.082;0.082;
0.082;0.082;0.082;0.082;0.082;0.082;0.082;0.082;0.083;0.083;0.083;0.083;0.083;0.083;0.083;0.084;
0.084;0.084;0.085;0.085;0.086;0.086;0.087;0.088;0.088;0.089;0.089;0.089;0.089;0.089;0.089;0.089;
0.089;0.089;0.089;0.090;0.090;0.090;0.090;0.090;0.090;0.090;0.090;0.090;0.091;0.091;0.091;0.092;
0.092;0.092;0.092;0.092;0.093;0.093;0.093;0.093;0.093;0.093;0.094;0.094;0.094;0.095;0.095;0.096;
0.096;0.096;0.097;0.097;0.097;0.097;0.097;0.097;0.097;0.098;0.098;0.098;0.098;0.098;0.099;0.099;
0.099;0.099;0.099;0.100;0.100;0.100;0.100;0.100;0.100;0.100;0.100;0.100;0.100;0.100;0.100;0.100;
0.100;0.100;0.101;0.101;0.101;0.101;0.101;0.101;0.101;0.101;0.101;0.102;0.102;0.102;0.102;0.102;
0.102;0.102;0.102;0.103;0.103;0.103;0.103;0.103;0.103;0.103;0.104;0.104;0.105;0.105;0.105;0.105;
0.105;0.106;0.106;0.106;0.106;0.107;0.107;0.107;0.108;0.108;0.108;0.108;0.108;0.108;0.108;0.109;
0.109;0.109;0.109;0.109;0.109;0.109;0.110;0.110;0.110;0.110;0.110;0.110;0.110;0.110;0.110;0.110;
0.110;0.111;0.111;0.111;0.111;0.112;0.112;0.112;0.112;0.112;0.114;0.114;0.114;0.115;0.115;0.115;
0.117;0.119;0.119;0.119;0.119;0.120;0.120;0.120;0.121;0.122;0.122;0.123;0.123;0.125;0.125;0.128;
0.129;0.129;0.129;0.130;0.131];
[H, P, CRITVAL] = bootmode (x, 1, 2000, 'Gaussian');
[H, P, CRITVAL] = bootmode (x, 2, 2000, 'Gaussian');
% bootwild
% bootwild:test:1
H0 = 150;
heights = [183, 192, 182, 183, 177, 185, 188, 188, 182, 185].';
stats = bootwild (heights - H0);
stats = bootwild (heights - H0, ones(10,1));
stats = bootwild (heights - H0, [], 2);
stats = bootwild (heights - H0, [], [1;1;2;2;3;3;4;4;5;5]);
stats = bootwild (heights - H0, [], [], 2000);
stats = bootwild (heights - H0, [], [], [], 0.05);
stats = bootwild (heights - H0, [], [], [], [0.025, 0.975]);
stats = bootwild (heights - H0, [], [], [], [], 1);
stats = bootwild (heights - H0, [], [], [], [], []);
stats = bootwild (heights - H0, [], [], [], [], [], 1);
stats = bootwild (heights - H0, [], [], [], [], [], []);
[stats,bootstat] = bootwild (heights - H0);
% bootwild:test:2
X = [ones(43,1),...
[01,02,03,04,05,06,07,08,09,10,11,...
12,13,14,15,16,17,18,19,20,21,22,...
23,25,26,27,28,29,30,31,32,33,34,...
35,36,37,38,39,40,41,42,43,44]'];
y = [188.0,170.0,189.0,163.0,183.0,171.0,185.0,168.0,173.0,183.0,173.0,...
173.0,175.0,178.0,183.0,192.4,178.0,173.0,174.0,183.0,188.0,180.0,...
168.0,170.0,178.0,182.0,180.0,183.0,178.0,182.0,188.0,175.0,179.0,...
183.0,192.0,182.0,183.0,177.0,185.0,188.0,188.0,182.0,185.0]';
stats = bootwild (y, X);
stats = bootwild (y, X, 4);
stats = bootwild (y, X, [], 2000);
stats = bootwild (y, X, [], [], 0.05);
stats = bootwild (y, X, [], [], [0.025, 0.975]);
stats = bootwild (y, X, [], [], [], 1);
stats = bootwild (y, X, [], [], [], []);
stats = bootwild (y, X, [], [], [], [], 1);
stats = bootwild (y, X, [], [], [], [], []);
[stats, bootstat] = bootwild (y, X);
% bootbayes
% bootbayes:test:1
heights = [183, 192, 182, 183, 177, 185, 188, 188, 182, 185].';
stats = bootbayes (heights);
stats = bootbayes (repmat (heights, 1, 5));
stats = bootbayes (heights, ones (10, 1));
stats = bootbayes (heights, [], 2);
stats = bootbayes (heights, [], [1;1;2;2;3;3;4;4;5;5]);
stats = bootbayes (heights, [], [], 2000);
stats = bootbayes (heights, [], [], [], 0.05);
stats = bootbayes (heights, [], [], [], [0.025, 0.975]);
stats = bootbayes (heights, [], [], [], []);
stats = bootbayes (heights, [], [], [], [], [], []);
[stats,bootstat] = bootbayes (heights);
% bootbayes:test:2
X = [ones(43,1),...
[01,02,03,04,05,06,07,08,09,10,11,...
12,13,14,15,16,17,18,19,20,21,22,...
23,25,26,27,28,29,30,31,32,33,34,...
35,36,37,38,39,40,41,42,43,44]'];
y = [188.0,170.0,189.0,163.0,183.0,171.0,185.0,168.0,173.0,183.0,173.0,...
173.0,175.0,178.0,183.0,192.4,178.0,173.0,174.0,183.0,188.0,180.0,...
168.0,170.0,178.0,182.0,180.0,183.0,178.0,182.0,188.0,175.0,179.0,...
183.0,192.0,182.0,183.0,177.0,185.0,188.0,188.0,182.0,185.0]';
stats = bootbayes (y, X);
stats = bootbayes (y, X, 4);
stats = bootbayes (y, X, [], 2000);
stats = bootbayes (y, X, [], [], 0.05);
stats = bootbayes (y, X, [], [], [0.025, 0.975]);
stats = bootbayes (y, X, [], []);
[stats, bootstat] = bootbayes (y, X);
% bootlm
% bootlm:test:1
% Two-sample unpaired test on independent samples (equivalent to Welch's
% t-test).
score = [54 23 45 54 45 43 34 65 77 46 65]';
gender = {'male' 'male' 'male' 'male' 'male' 'female' 'female' 'female' ...
'female' 'female' 'female'}';
STATS = bootlm (score, gender, 'display', 'off', 'varnames', 'gender', ...
'dim', 1, 'posthoc','trt_vs_ctrl');
STATS = bootlm (score, gender, 'display', 'off', 'varnames', 'gender', ...
'dim', 1, 'method', 'bayesian', 'prior', 'auto');
% bootlm:test:2
% Two-sample paired test on dependent or matched samples equivalent to a
% paired t-test.
score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
treatment = {'before' 'after'; 'before' 'after'; 'before' 'after';
'before' 'after'; 'before' 'after'}';
subject = {'GS' 'GS'; 'JM' 'JM'; 'HM' 'HM'; 'JW' 'JW'; 'PS' 'PS'}';
STATS = bootlm (score(:), {treatment(:), subject(:)}, ...
'model', 'linear', 'display', 'off', ...
'varnames', {'treatment', 'subject'}, ...
'dim', 1, 'posthoc','trt_vs_ctrl');
STATS = bootlm (score(:), {treatment(:), subject(:)}, ...
'model', 'linear', 'display', 'off', ...
'varnames', {'treatment', 'subject'}, ...
'dim', 1, 'method','bayesian', 'prior', 'auto');
% bootlm:test:3
% One-way design. The data is from a study on the strength of structural
% beams, in Hogg and Ledolter (1987) Engineering Statistics. NY: MacMillan
strength = [82 86 79 83 84 85 86 87 74 82 ...
78 75 76 77 79 79 77 78 82 79]';
alloy = {'st','st','st','st','st','st','st','st', ...
'al1','al1','al1','al1','al1','al1', ...
'al2','al2','al2','al2','al2','al2'}';
STATS = bootlm (strength, alloy, 'display', 'off', 'varnames', 'alloy', ...
'dim', 1, 'posthoc','pairwise');
STATS = bootlm (strength, alloy, 'display', 'off', 'varnames', 'alloy', ...
'dim', 1, 'method','bayesian', 'prior', 'auto');
% bootlm:test:4
% One-way repeated measures design. The data is from a study on the number of
% words recalled by 10 subjects for three time condtions, in Loftus & Masson
% (1994) Psychon Bull Rev. 1(4):476-490, Table 2.
words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
15 17 17; 1 1 4; 12 15 17; 9 12 12; 8 9 12];
seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
subject = [ 1 1 1; 2 2 2; 3 3 3; 4 4 4; 5 5 5; ...
6 6 6; 7 7 7; 8 8 8; 9 9 9; 10 10 10];
[STATS, BOOTSTAT, AOVSTAT] = bootlm (words(:), ...
{subject(:), seconds(:)}, 'model', ...
'linear', 'display', 'off', ...
'varnames', {'subject', 'seconds'});
STATS = bootlm (words(:), {subject(:), seconds(:)}, ...
'model', 'linear', 'display', 'off', ...
'varnames', {'subject', 'seconds'}, ...
'dim', 2, 'posthoc', 'pairwise');
STATS = bootlm (words(:), {subject(:), seconds(:)}, ...
'model', 'linear', 'display', 'off', ...
'varnames', {'subject', 'seconds'}, ...
'dim', 2, 'method', 'bayesian', 'prior', 'auto');
% bootlm:test:5
% Balanced two-way design with interaction on the data from a study of popcorn
% brands and popper types, in Hogg and Ledolter (1987) Engineering Statistics.
% New York: MacMillan
% Balanced two-way design. The data is yield of cups of popped popcorn from
% different popcorn brands and popper types, in Hogg and Ledolter (1987)
% Engineering Statistics. NY: MacMillan
popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
brands = {'Gourmet', 'National', 'Generic'; ...
'Gourmet', 'National', 'Generic'; ...
'Gourmet', 'National', 'Generic'; ...
'Gourmet', 'National', 'Generic'; ...
'Gourmet', 'National', 'Generic'; ...
'Gourmet', 'National', 'Generic'};
popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
STATS = bootlm (popcorn(:), {brands(:), popper(:)}, ...
'display', 'off', 'model', 'full', ...
'varnames', {'brands', 'popper'});
STATS = bootlm (popcorn(:), {brands(:), popper(:)}, ...
'display', 'off', 'model', 'full', ...
'varnames', {'brands', 'popper'}, ...
'dim', 1, 'posthoc', 'pairwise');
STATS = bootlm (popcorn(:), {brands(:), popper(:)}, ...
'display', 'off', 'model', 'full', ...
'varnames', {'brands', 'popper'}, ...
'dim', 1, 'method', 'bayesian', 'prior', 'auto');
STATS = bootlm (popcorn(:), {brands(:), popper(:)}, ...
'display', 'off', 'model', 'full', ...
'varnames', {'brands', 'popper'}, ...
'dim', 2, 'posthoc', 'pairwise');
STATS = bootlm (popcorn(:), {brands(:), popper(:)}, ...
'display', 'off', 'model', 'full', ...
'varnames', {'brands', 'popper'}, ...
'dim', 2, 'method', 'bayesian', 'prior', 'auto');
% bootlm:test:6
% Unbalanced two-way design (2x2). The data is from a study on the effects
% of gender and a college degree on starting salaries of company employees,
% in Maxwell, Delaney and Kelly (2018): Chapter 7, Table 15
salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
25 29 27 19 18 21 20 21 22 19]';
gender = {'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f'...
'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm'}';
degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
% ANOVA (including the main effect of gender averaged over levels of degree)
[STATS, BOOTSTAT, AOVSTAT] = bootlm (salary, {degree, gender}, 'model', ...
'full', 'display', 'off', 'varnames', ...
{'degree', 'gender'});
[STATS, BOOTSTAT, AOVSTAT] = bootlm (salary, {gender, degree}, 'model', ...
'full', 'display', 'off', 'varnames', ...
{'gender', 'degree'});
STATS = bootlm (salary, {gender, degree}, 'model', 'full', ...
'display', 'off', 'varnames', ...
{'gender', 'degree'});
STATS = bootlm (salary, {gender, degree}, 'model', 'full', ...
'display', 'off', 'varnames', ...
{'gender', 'degree'}, 'dim', 1, ...
'posthoc', 'trt_vs_ctrl');
STATS = bootlm (salary, {gender, degree}, 'model', 'full', ...
'display', 'off', 'varnames', ...
{'gender', 'degree'}, 'dim', 1, ...
'method', 'bayesian', 'prior', 'auto');
STATS = bootlm (salary, {gender, degree}, 'model', 'full', ...
'display', 'off', 'varnames', ...
{'gender', 'degree'}, 'dim', 2, ...
'posthoc', 'trt_vs_ctrl');
STATS = bootlm (salary, {gender, degree}, 'model', 'full', ...
'display', 'off', 'varnames', ...
{'gender', 'degree'}, 'dim', 2, ...
'method', 'bayesian','prior', 'auto');
% bootlm:test:7
% Unbalanced two-way design (3x2) on the data from a study of the effect of
% adding sugar and/or milk on the tendency of coffee to make people babble,
% in from Navarro (2019): 16.10
sugar = {'real' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'none' ...
'fake' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'fake'}';
milk = {'yes' 'no' 'no' 'yes' 'yes' 'no' 'yes' 'yes' 'yes' ...
'no' 'no' 'yes' 'no' 'no' 'no' 'no' 'no' 'yes'}';
babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
STATS = bootlm (babble, {sugar, milk}, 'model', 'full', 'display', 'off', ...
'varnames', {'sugar', 'milk'});
% bootlm:test:8
% Unbalanced three-way design (3x2x2). The data is from a study of the
% effects of three different drugs, biofeedback and diet on patient blood
% pressure, adapted* from Maxwell, Delaney and Kelly (2018): Ch 8, Table 12
drug = {'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' ...
'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X';
'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' ...
'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y';
'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' ...
'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z'};
feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
173 194 197 190 176 198 164 190 169 164 176 175;
186 194 201 215 219 209 164 166 159 182 187 174 ...
189 194 217 206 199 195 171 173 196 199 180 203;
180 187 199 170 204 194 162 184 183 156 180 173 ...
202 228 190 206 224 204 205 199 170 160 179 179];
[STATS, BOOTSTAT, AOVSTAT] = bootlm (BP(:), {drug(:), feedback(:), ...
diet(:)}, 'seed', 1, ...
'model', 'full', 'display', 'off', ...
'varnames', {'drug', 'feedback', 'diet'});
STATS = bootlm (BP(:), {drug(:), feedback(:), diet(:)}, ...
'model', 'full', ...
'display', 'off', ...
'varnames', {'drug', 'feedback', 'diet'});
STATS = bootlm (BP(:), {drug(:), feedback(:), diet(:)}, 'model', 'full', ...
'display', 'off', 'dim', [1,2,3], ...
'posthoc', 'trt_vs_ctrl', ...
'varnames', {'drug', 'feedback', 'diet'});
STATS = bootlm (BP(:), {drug(:), feedback(:), diet(:)}, 'model', 'full', ...
'display', 'off', 'dim', [1,2,3], ...
'method', 'bayesian', 'prior', 'auto', ...
'varnames', {'drug', 'feedback', 'diet'});
% bootlm:test:9
% One-way design with continuous covariate on data from a study of the
% additive effects of species and temperature on chirpy pulses of crickets,
% from Stitch, The Worst Stats Text eveR
pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};
STATS = bootlm (pulse, {species, temp}, 'model', 'linear', ...
'continuous', 2, 'display', 'off', ...
'varnames', {'species', 'temp'});
% bootlm:test:10
% Factorial design with continuous covariate on data from a study of the
% effects of treatment and exercise on stress reduction score after adjusting
% for age. Data from R datarium package).
score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' ...
'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' ...
'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no'}';
exercise = {'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' ...
'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' ...
'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' ...
'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi'}';
age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
STATS = bootlm (score, {treatment, exercise, age}, ...
'model', [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
'continuous', 3, 'display', 'off', ...
'varnames', {'treatment', 'exercise', 'age'});
STATS = bootlm (score, {treatment, exercise, age}, 'seed', 1, ...
'model', [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
'continuous', 3, 'display', 'off', ...
'varnames', {'treatment', 'exercise', 'age'},...
'dim', [1, 2]);
STATS = bootlm (score, {treatment, exercise, age}, 'seed', 1, ...
'model', [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
'continuous', 3, 'display', 'off', ...
'varnames', {'treatment', 'exercise', 'age'},...
'dim', [1, 2], 'posthoc', 'trt_vs_ctrl');
% bootlm:test:11
% One-way design with continuous covariate. The data is from a study of the
% additive effects of species and temperature on chirpy pulses of crickets,
% from Stitch, The Worst Stats Text eveR
pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};
[STATS, BOOTSTAT, AOVSTAT] = bootlm (pulse, {temp, species}, 'model', ...
'linear', 'continuous', 1, 'display', 'off', ...
'varnames', {'temp', 'species'});
STATS = bootlm (pulse, {temp, species}, 'model', 'linear', ...
'continuous', 1, 'display', 'off', ...
'varnames', {'temp', 'species'});
STATS = bootlm (pulse, {temp, species}, 'model', 'linear', ...
'continuous', 1, 'display', 'off', ...
'varnames', {'temp', 'species'}, 'dim', 2, ...
'posthoc', 'trt_vs_ctrl');
STATS = bootlm (pulse, {temp, species}, 'model', 'linear', ...
'continuous', 1, 'display', 'off', ...
'varnames', {'temp', 'species'}, 'dim', 2, ...
'method', 'bayesian', 'prior', 'auto');
% bootlm:test:11
% Factorial design with continuous covariate. The data is from a study of the
% effects of treatment and exercise on stress reduction score after adjusting
% for age. Data from R datarium package).
score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' ...
'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' ...
'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no' 'no'}';
exercise = {'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' ...
'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' ...
'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' 'lo' ...
'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi' 'hi'}';
age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
[STATS, BOOTSTAT, AOVSTAT] = bootlm (score, {age, exercise, treatment}, ...
'model', [1 0 0; 0 1 0; 0 0 1; 0 1 1], ...
'continuous', 1, 'display', 'off', ...
'varnames', {'age', 'exercise', 'treatment'});
STATS = bootlm (score, {age, exercise, treatment}, ...
'model', [1 0 0; 0 1 0; 0 0 1; 0 1 1], ...
'continuous', 1, 'display', 'off', ...
'varnames', {'age', 'exercise', 'treatment'});
STATS = bootlm (score, {age, exercise, treatment}, ...
'model', [1 0 0; 0 1 0; 0 0 1; 0 1 1], ...
'continuous', 1, 'display', 'off', ...
'varnames', {'age', 'exercise', 'treatment'}, ...
'dim', [2, 3], 'posthoc', 'trt_vs_ctrl');
STATS = bootlm (score, {age, exercise, treatment}, 'dim', [2, 3], ...
'model', [1 0 0; 0 1 0; 0 0 1; 0 1 1], ...
'continuous', 1, 'display', 'off', ...
'varnames', {'age', 'exercise', 'treatment'}, ...
'method', 'bayesian', 'prior', 'auto');
% bootlm:test:12
% Unbalanced one-way design with custom, orthogonal contrasts. The statistics
% relating to the contrasts are shown in the table of model parameters, and
% can be retrieved from the STATS.coeffs output.
dv = [ 8.706 10.362 11.552 6.941 10.983 10.092 6.421 14.943 15.931 ...
22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
25.694 ]';
g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
C = [ 0.4001601 0.3333333 0.5 0.0
0.4001601 0.3333333 -0.5 0.0
0.4001601 -0.6666667 0.0 0.0
-0.6002401 0.0000000 0.0 0.5
-0.6002401 0.0000000 0.0 -0.5];
STATS = bootlm (dv, g, 'contrasts', C, 'varnames', 'score', ...
'alpha', 0.05, 'display', false);
STATS = bootlm (dv, g, 'contrasts', C, 'varnames', 'score', ...
'alpha', 0.05, 'display', false, 'dim', 1, ...
'method', 'Bayesian', 'prior', 'auto');
% bootlm:test:13
% One-way design.
g = [1, 1, 1, 1, 1, 1, 1, 1, ...
2, 2, 2, 2, 2, 2, 2, 2, ...
3, 3, 3, 3, 3, 3, 3, 3]';
y = [13, 16, 16, 7, 11, 5, 1, 9, ...
10, 25, 66, 43, 47, 56, 6, 39, ...
11, 39, 26, 35, 25, 14, 24, 17]';
stats = bootlm (y, g, 'display', false, 'dim', 1, 'posthoc', 'pairwise', ...
'seed', 1);
% bootlm:test:14
% Prediction errors of linear models
amount = [25.8; 20.5; 14.3; 23.2; 20.6; 31.1; 20.9; 20.9; 30.4; ...
16.3; 11.6; 11.8; 32.5; 32.0; 18.0; 24.1; 26.5; 25.8; ...
28.8; 22.0; 29.7; 28.9; 32.8; 32.5; 25.4; 31.7; 28.5];
hrs = [99; 152; 293; 155; 196; 53; 184; 171; 52; ...
376; 385; 402; 29; 76; 296; 151; 177; 209; ...
119; 188; 115; 88; 58; 49; 150; 107; 125];
lot = {'A'; 'A'; 'A'; 'A'; 'A'; 'A'; 'A'; 'A'; 'A'; ...
'B'; 'B'; 'B'; 'B'; 'B'; 'B'; 'B'; 'B'; 'B'; ...
'C'; 'C'; 'C'; 'C'; 'C'; 'C'; 'C'; 'C'; 'C'};
[stats, bootstat, aovstat, pred_err] = bootlm (amount, {hrs, lot}, ...
'continuous', 1, 'seed', 1, ...
'model', 'linear', 'display', 'off', ...
'varnames', {'hrs', 'lot'}, ...
'contrasts', 'treatment');
% bootlm:test:15
% Comparing analysis of nested design using ANOVA with clustered resampling.
% Two factor nested model example from:
% https://www.southampton.ac.uk/~cpd/anovas/datasets/#Chapter2
data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
clustid = [1 3 5; 1 3 5; 1 3 5; 1 3 5; ...
2 4 6; 2 4 6; 2 4 6; 2 4 6];
group = {'A' 'B' 'C'; 'A' 'B' 'C'; 'A' 'B' 'C'; 'A' 'B' 'C'; ...
'A' 'B' 'C'; 'A' 'B' 'C'; 'A' 'B' 'C'; 'A' 'B' 'C'};
[stats, bootstat, aovstat] = bootlm (data(:), group(:), 'seed', 1, ...
'clustid', clustid(:), 'display', 'off');
% randtest:test:1
% Test various capabilities of randtest
X = randn (3,1);
Y = randn (3,1);
pval1 = randtest (X, Y);
pval2 = randtest (X, Y, 500);
randtest (X, Y, [], []);
X = randn (9,1);
Y = randn (9,1);
pval3 = randtest (X, Y, 5000);
pval4 = randtest (X, Y, [], [], 1);
pval5 = randtest (X, Y, [], @mldivide, 1);
pval6 = randtest (X, Y, [], @cor, 1);
% randtest1:test:1
A = randn (3,1);
M = 0;
pval1 = randtest1 (A, M);
pval2 = randtest1 (A, M, []);
randtest1 (A, M, 500);
randtest1 (A, M, []);
A = randn (9,1);
pval3 = randtest1 (A, M, 5000);
pval4 = randtest1 (A, M, [], [], 1);
pval5 = randtest1 (A, M, [], 'mean', 1);
pval6 = randtest1 (A, M, [], @smoothmedian, 1);
% randtest2:test:1
X = randn (3,1);
Y = randn (3,1);
pval1 = randtest2 (X, Y);
pval2 = randtest2 (X, Y, false);
pval3 = randtest2 (X, Y, []);
randtest2 (X, Y, true);
randtest2 (X, Y, [], 500);
randtest2 (X, Y, [], []);
X = randn (9,1);
Y = randn (9,1);
pval5 = randtest2 (X, Y, false, 5000);
pval5 = randtest2 (X, Y, false, [], [], 1);
pval6 = randtest2 (X, Y, false, [], @(X, Y) mean (X) - mean (Y), 1);
pval7 = randtest2 (X, Y, false, [], @(A, B) log (var (A) ./ var (B)), 1);
% credint:test:1
heights = [183, 192, 182, 183, 177, 185, 188, 188, 182, 185].';
[stats, bootstat] = bootbayes (heights);
CI_1 = credint (bootstat,0.95); % 95% shortest probability interval
CI_2 = credint (bootstat,[0.025,0.975]); % 95% equal-tailed interval
% bootint:test:1
data = [576, 3.39; 635, 3.30; 558, 2.81; 578, 3.03; 666, 3.44; ...
580, 3.07; 555, 3.00; 661, 3.43; 661, 3.36; 605, 3.13; ...
653, 3.12; 575, 2.74; 545, 2.76; 572, 2.88; 594, 2.96];
x = data(:, 1);
y = data(:, 2);
r = cor (x, y);
bootstat = bootstrp (4999, @cor, x, y);
CI_1 = bootint (bootstat,0.95); % 95% percentile interval
CI_2 = bootint (bootstat,[0.025,0.975]); % 95% percentile interval
CI_3 = bootint (bootstat,0.95,r); % 95% bias-corrected interval
CI_4 = bootint (bootstat,[0.025,0.975],r); % 95% bias-corrected interval
% sampszcalc:test:1
ns = sampszcalc ('t', 0.20, 0.80, 0.05, 2);
nm = sampszcalc ('t', 0.50, 0.80, 0.05, 2);
nl = sampszcalc ('t', 0.80, 0.80, 0.05, 2);
ns = sampszcalc ('t', 'small');
nm = sampszcalc ('t', 'medium');
nl = sampszcalc ('t', 'large');
% sampszcalc:test:2
ns = sampszcalc ('t2', 0.20, 0.80, 0.05, 2);
nm = sampszcalc ('t2', 0.50, 0.80, 0.05, 2);
nl = sampszcalc ('t2', 0.80, 0.80, 0.05, 2);
ns = sampszcalc ('t2', 'small');
nm = sampszcalc ('t2', 'medium');
nl = sampszcalc ('t2', 'large');
% sampszcalc:test:3
ns = sampszcalc ('z', 0.20, 0.80, 0.05, 2);
nm = sampszcalc ('z', 0.50, 0.80, 0.05, 2);
nl = sampszcalc ('z', 0.80, 0.80, 0.05, 2);
ns = sampszcalc ('z', 'small');
nm = sampszcalc ('z', 'medium');
nl = sampszcalc ('z', 'large');
% sampszcalc:test:4
ns = sampszcalc ('z2', 0.20, 0.80, 0.05, 2);
nm = sampszcalc ('z2', 0.50, 0.80, 0.05, 2);
nl = sampszcalc ('z2', 0.80, 0.80, 0.05, 2);
ns = sampszcalc ('z2', 'small');
nm = sampszcalc ('z2', 'medium');
nl = sampszcalc ('z2', 'large');
% sampszcalc:test:5
ns = sampszcalc ('r', 0.10, 0.80, 0.05, 2);
nm = sampszcalc ('r', 0.30, 0.80, 0.05, 2);
nl = sampszcalc ('r', 0.50, 0.80, 0.05, 2);
ns = sampszcalc ('r', 'small');
nm = sampszcalc ('r', 'medium');
nl = sampszcalc ('r', 'large');
% sampszcalc:test:6
score = [21, 26, 33, 22, 18, 25, 26, 24, 21, 25, 35, 28, 32, 36, 38, ...
26, 34, 27, 38, 44, 34, 45, 38, 31, 41, 34, 35, 38, 46]';
method = {'A','A','A','A','A','A','A','A','A','A','A','A','A','A','A', ...
'B','B','B','B','B','B','B','B','B','B','B','B','B','B'}';
room = [1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, ...
1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3]';
[STATS_STD] = bootlm (score, {method}, 'clustid', room, ...
'seed', 1, 'display', 'off', 'dim', 1, ...
'posthoc', 'trt_vs_ctrl', ...
'method', 'bayesian', 'prior', 'auto', ...
'standardize', true);
[STATS, BOOTSTAT] = bootlm (score, {method}, 'clustid', room, ...
'seed', 1, 'display', 'off', 'dim', 1, ...
'posthoc', 'trt_vs_ctrl', ...
'method', 'bayesian', 'prior', 'auto');
[STATS_SRS, BOOTSTAT_SRS] = bootlm (score, {method}, 'clustid', [], ...
'seed', 1, 'display', 'off', 'dim', 1, ...
'posthoc', 'trt_vs_ctrl', ...
'method', 'bayesian', 'prior', 'auto');
N = sampszcalc ('t2', STATS_STD.estimate, 0.80, 0.05, 2);
DEFF = deffcalc (BOOTSTAT, BOOTSTAT_SRS);
N_corrected = sampszcalc ('t2', STATS_STD.estimate, 0.80, 0.05, 2, DEFF);
fprintf('Tests completed successfully.\n')
catch
fprintf('Tests were unsuccessful.\n')
rethrow(lasterror)
end