Experiments DB Dev
378ad6da05
Add variation_logpower.m (self-contained) + make_variation_logpower.m, dropping logpowersim.m + logpower_result.txt into all 28 variation folders. Matches the paper's power code: models behavior ~ stim + log(day) + stim:log(day) + (1|rat) (log(day+1), since our day 0 = paper Day 1), reports Cohen's f (partial eta^2 of the interaction) and the interaction under residual/Satterthwaite/honest random-slope DF, then runs the Monte-Carlo power sim on the log-day ground truth (per-animal cluster-honest + LME power). Notable: under log(day) the accumulating divergence is captured more sharply, so several full-window scenarios reach honest significance that were n.s. under raw day (e.g. right_only_d0_13 honest p=0.003, unmerge_d0_13 0.021, unmerge_d0_10 0.036); Cohen's f is small-medium (~0.10-0.34). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
29 lines
1008 B
Matlab
29 lines
1008 B
Matlab
function make_variation_logpower()
|
|
%MAKE_VARIATION_LOGPOWER Add the log-day model + Cohen's f + power to each variation.
|
|
% MAKE_VARIATION_LOGPOWER() copies variation_logpower.m into every
|
|
% variations/<name>/ folder with a data.csv (as logpowersim.m) and runs it,
|
|
% producing logpower_result.txt. Re-runnable; picks up new folders.
|
|
|
|
thisDir = fileparts(mfilename('fullpath'));
|
|
template = fullfile(thisDir, 'variation_logpower.m');
|
|
root = fullfile(thisDir, 'variations');
|
|
|
|
d = dir(root);
|
|
n = 0;
|
|
for i = 1:numel(d)
|
|
if ~d(i).isdir || ismember(d(i).name, {'.', '..'}); continue; end
|
|
folder = fullfile(root, d(i).name);
|
|
if ~exist(fullfile(folder, 'data.csv'), 'file'); continue; end
|
|
copyfile(template, fullfile(folder, 'logpowersim.m'));
|
|
fprintf('\n### %s ###\n', d(i).name);
|
|
localRun(fullfile(folder, 'logpowersim.m'));
|
|
n = n + 1;
|
|
end
|
|
fprintf('\nAdded logpowersim.m + logpower_result.txt to %d variation folders.\n', n);
|
|
|
|
end
|
|
|
|
function localRun(scriptPath)
|
|
run(scriptPath);
|
|
end
|