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added another test
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@acp29
acp29 committed Apr 19, 2024
1 parent 48f74d6 commit 3aed783
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23 changes: 23 additions & 0 deletions inst/sampszcalc.m
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Expand Up @@ -365,3 +365,26 @@
%! nl = sampszcalc ('r', 'large');
%! assert (nl, 30, 1);

%!test
%! 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);
86 changes: 73 additions & 13 deletions test/test_script.m
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Expand Up @@ -336,7 +336,6 @@
% 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'}';
Expand All @@ -349,7 +348,6 @@
'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
Expand All @@ -362,7 +360,6 @@
'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
Expand All @@ -385,7 +382,6 @@
'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.
Expand Down Expand Up @@ -422,7 +418,6 @@
'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,
Expand Down Expand Up @@ -459,7 +454,6 @@
'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,
Expand All @@ -473,7 +467,6 @@

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
Expand Down Expand Up @@ -529,7 +522,6 @@
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
Expand Down Expand Up @@ -569,7 +561,6 @@
'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,
Expand Down Expand Up @@ -597,7 +588,6 @@
'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
Expand Down Expand Up @@ -641,7 +631,6 @@
'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
Expand All @@ -663,7 +652,6 @@
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, ...
Expand All @@ -674,7 +662,6 @@
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; ...
Expand Down Expand Up @@ -707,6 +694,20 @@
[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);
pval5 = randtest (X, Y, 5000);
pval5 = randtest (X, Y, [], [], 1);
pval6 = randtest (X, Y, [], @mldivide, 1);
pval7 = randtest (X, Y, [], @cor, 1);

% randtest2:test:1
X = randn (3,1);
Y = randn (3,1);
Expand All @@ -729,6 +730,65 @@
CI = credint (Y,0.95); % Shortest probability interval
CI = credint (Y,[0.025,0.975]); % Equal-tailed 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
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